## Callin Switzer ## 17 Nov 2017 ## Multilevel model to visualize bees' ## behavior on the artificial pollen system ## Update 30 July 2018 -- investigate proportion of time rewarded
#install packages
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if(length(new.pkg)) install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
}
packages <- c("tidyverse", "reshape2", 'lme4', 'sjPlot',
"multcomp", "effects", "cowplot")
ipak(packages)## Loading required package: tidyverse## ── Attaching packages ──────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.0.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.6
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0## ── Conflicts ─────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()## Loading required package: reshape2##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smiths## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:tidyr':
##
## expand## Loading required package: sjPlot## Learn more about sjPlot with 'browseVignettes("sjPlot")'.## Loading required package: multcomp## Loading required package: mvtnorm## Loading required package: survival## Loading required package: TH.data## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select##
## Attaching package: 'TH.data'## The following object is masked from 'package:MASS':
##
## geyser## Loading required package: effects## Loading required package: carData## lattice theme set by effectsTheme()
## See ?effectsTheme for details.## Loading required package: cowplot##
## Attaching package: 'cowplot'## The following objects are masked from 'package:sjPlot':
##
## plot_grid, save_plot## The following object is masked from 'package:ggplot2':
##
## ggsave## tidyverse reshape2 lme4 sjPlot multcomp effects cowplot
## TRUE TRUE TRUE TRUE TRUE TRUE TRUE# set ggplot theme
theme_set(theme_classic() + theme(axis.text=element_text(colour="black"), text=element_text(size=10)))
# define data and figure directories
{if(windows){
# windows
dataDir <- "D:/Dropbox/SonicationBehavior/SonBehData"
figDir <- "D:/Dropbox/SonicationBehavior/SonBehFigs"
}
else{
# mac
dataDir <- "/Users/cswitzer/Dropbox/SonicationBehavior/SonBehData"
figDir <- "/Users/cswitzer/Dropbox/SonicationBehavior/SonBehFigs"
}}
# check if the directory exists
file.info(dataDir)$isdir## [1] TRUEprint(paste("last run ", Sys.time()))## [1] "last run 2018-10-11 11:14:50"print(R.version)## _
## platform x86_64-apple-darwin15.6.0
## arch x86_64
## os darwin15.6.0
## system x86_64, darwin15.6.0
## status
## major 3
## minor 5.1
## year 2018
## month 07
## day 02
## svn rev 74947
## language R
## version.string R version 3.5.1 (2018-07-02)
## nickname Feather SprayRead in data and double check it
sl <- read_csv(file.path(dataDir, '01_CombinedTrials_cleaned.csv'))## Parsed with column specification:
## cols(
## .default = col_integer(),
## beeCol = col_character(),
## meanFreq = col_double(),
## IT_imputed = col_double(),
## freq = col_double(),
## amp = col_double(),
## datetime = col_character(),
## rewTF = col_logical(),
## BeeNumCol = col_character(),
## accFile = col_character(),
## datetime_str = col_datetime(format = ""),
## IT = col_double(),
## trt = col_character(),
## amp_acc = col_double()
## )## See spec(...) for full column specifications.colnames(sl)## [1] "beeCol" "hive" "meanFreq" "IT_imputed"
## [5] "index" "freq" "amp" "datetime"
## [9] "rewNum" "rewTF" "lowRewAmp" "highrewAmp"
## [13] "BeeNumCol" "accFile" "trialNum" "datetime_str"
## [17] "lowFrq" "highFrq" "IT" "trt"
## [21] "amp_acc"sl <- sl %>%
# gets rid of the copious warnings
mutate(timeSinceStart = NA, timeSinceLastBuzz = NA, buzzesSinceReward = NA) %>%
# make sure all bee colors are lowercase
mutate(beeCol = tolower(beeCol)) %>%
# fix orange.5, which should be orange.3
mutate(hive = ifelse(hive == "5" & beeCol == "orange", "3", hive)) %>%
# color + hive is an ID var
mutate(beeColHive = interaction(beeCol, hive),
colNum = paste(beeCol, hive, sep = "_")) %>%
# remove two trials I messed up
filter(!(beeCol == "whitepink" & trialNum == 3)) %>%
filter(!(beeCol == "limepurple" & trialNum == 3)) %>%
# convert hive to factor
mutate(hive = as.factor(hive),
trt2 = ifelse(trt == 'full' & trialNum > 1, "full_2", as.character(trt)),
trt = relevel(factor(trt), ref = "full")) %>%
# divide amplitud by 2
mutate(amp_acc2 = amp_acc / 2) %>%
mutate(trialNum0 = trialNum - 1)
initialFreqAmp <- sl %>%
dplyr::select(beeColHive, trialNum, freq, amp_acc2) %>%
filter(trialNum == 1) %>%
group_by(beeColHive) %>%
summarize(initialFreq = mean(freq),
initialAmp2 = mean(amp_acc2)) %>%
full_join(sl) %>%
mutate(ampDiff = amp_acc2 - initialAmp2,
freqDiff = freq - initialFreq) %>%
dplyr::select(beeColHive, trialNum, freqDiff, ampDiff,
IT_imputed, rewTF, trt, hive, trt2,
freq, amp_acc2, trialNum0, initialFreq, initialAmp2) ## Joining, by = "beeColHive"# calculate proportion of rewards by trt
prop.table(xtabs(~ initialFreqAmp$rewTF + initialFreqAmp$trt), margin = 2)## initialFreqAmp$trt
## initialFreqAmp$rewTF full high low
## FALSE 0.0000000 0.6464258 0.7871369
## TRUE 1.0000000 0.3535742 0.2128631xtabs(~ initialFreqAmp$rewTF + initialFreqAmp$trt)## initialFreqAmp$trt
## initialFreqAmp$rewTF full high low
## FALSE 0 4205 9999
## TRUE 4968 2300 2704# number of trials per bee
sl %>%
group_by(beeColHive) %>%
summarize(count = n()) %>%
print(n = Inf)## # A tibble: 42 x 2
## beeColHive count
## <fct> <int>
## 1 gold.3 54
## 2 orange.3 501
## 3 purple.3 9
## 4 blue.4 36
## 5 goldred.4 3
## 6 green.4 13
## 7 redblue.4 673
## 8 white.4 283
## 9 whitegreen.4 39
## 10 lime.5 28
## 11 limeblue.5 530
## 12 limegold.5 54
## 13 limegreen.5 50
## 14 limeorange.5 84
## 15 limepink.5 51
## 16 limepurple.5 296
## 17 limepurpleyellow.5 58
## 18 limered.5 3474
## 19 limesilver.5 3
## 20 limewhite.5 101
## 21 limeyellow.5 52
## 22 orangeblue.5 50
## 23 orangegreen.5 50
## 24 orangepink.5 50
## 25 orangepurple.5 50
## 26 redgreen.5 530
## 27 redpink.5 677
## 28 redpurple.5 218
## 29 silver.5 26
## 30 whiteblue.5 2786
## 31 whitegold.5 1225
## 32 whiteorange.5 1122
## 33 whitepink.5 1113
## 34 whitepurple.5 55
## 35 whitered.5 1344
## 36 whiteyellow.5 157
## 37 yellowblue.5 1587
## 38 yellowgreen.5 532
## 39 yelloworange.5 2113
## 40 yellowpink.5 2309
## 41 yellowpurple.5 1243
## 42 yellowred.5 547hist(xtabs(~as.character(sl$beeColHive)))
max(xtabs(~sl$beeCol))## [1] 3474mean(xtabs(~sl$beeCol))## [1] 575.619sd(xtabs(~sl$beeCol))## [1] 828.9986# number of visits for first trial
xtabs(~sl$beeCol[sl$trialNum == 1])## sl$beeCol[sl$trialNum == 1]
## blue gold goldred green
## 36 54 3 9
## lime limeblue limegold limegreen
## 28 50 54 50
## limeorange limepink limepurple limepurpleyellow
## 33 51 53 58
## limered limesilver limewhite limeyellow
## 50 3 50 52
## orange orangeblue orangegreen orangepink
## 85 50 50 50
## orangepurple purple redblue redgreen
## 50 9 49 51
## redpink redpurple silver white
## 56 55 26 48
## whiteblue whitegold whitegreen whiteorange
## 56 48 39 54
## whitepink whitepurple whitered whiteyellow
## 64 55 59 55
## yellowblue yellowgreen yelloworange yellowpink
## 54 51 54 58
## yellowpurple yellowred
## 54 57hist(xtabs(~sl$beeCol[sl$trialNum == 1]))
mean(xtabs(~sl$beeCol[sl$trialNum == 1]))## [1] 46.92857sd(xtabs(~sl$beeCol[sl$trialNum == 1]))## [1] 16.37725# number of visits for second trial
xtabs(~sl$beeCol[sl$trialNum == 2])## sl$beeCol[sl$trialNum == 2]
## green limeblue limeorange limepurple limered
## 4 54 51 243 281
## limewhite redblue redgreen redpink redpurple
## 51 60 53 58 90
## white whiteblue whitegold whiteorange whitepink
## 67 314 66 260 143
## whitered whiteyellow yellowblue yellowgreen yelloworange
## 195 47 128 50 176
## yellowpink yellowpurple yellowred
## 142 346 56# number of trials per bee and treatment
xtabs(~sl$beeCol + sl$trt)## sl$trt
## sl$beeCol full high low
## blue 36 0 0
## gold 54 0 0
## goldred 3 0 0
## green 13 0 0
## lime 28 0 0
## limeblue 530 0 0
## limegold 54 0 0
## limegreen 50 0 0
## limeorange 84 0 0
## limepink 51 0 0
## limepurple 53 0 243
## limepurpleyellow 58 0 0
## limered 50 0 3424
## limesilver 3 0 0
## limewhite 101 0 0
## limeyellow 52 0 0
## orange 85 416 0
## orangeblue 50 0 0
## orangegreen 50 0 0
## orangepink 50 0 0
## orangepurple 50 0 0
## purple 9 0 0
## redblue 673 0 0
## redgreen 530 0 0
## redpink 56 0 621
## redpurple 55 163 0
## silver 26 0 0
## white 283 0 0
## whiteblue 56 0 2730
## whitegold 48 0 1177
## whitegreen 39 0 0
## whiteorange 54 0 1068
## whitepink 64 1049 0
## whitepurple 55 0 0
## whitered 59 1285 0
## whiteyellow 157 0 0
## yellowblue 54 1533 0
## yellowgreen 532 0 0
## yelloworange 54 2059 0
## yellowpink 58 0 2251
## yellowpurple 54 0 1189
## yellowred 547 0 0colSums(xtabs(~sl$beeCol + sl$trt))## full high low
## 4968 6505 12703# total num
sum(colSums(xtabs(~sl$beeCol + sl$trt)))## [1] 24176xtabs(~sl$beeCol + sl$IT_imputed)## sl$IT_imputed
## sl$beeCol 3.01 3.4 3.48 3.61 3.67 3.74 3.75 3.76 3.82 3.87 3.96
## blue 0 0 0 36 0 0 0 0 0 0 0
## gold 0 0 0 0 0 0 0 0 0 0 0
## goldred 3 0 0 0 0 0 0 0 0 0 0
## green 0 0 0 0 0 0 0 0 0 0 0
## lime 0 0 0 0 0 0 0 0 0 0 0
## limeblue 0 0 0 0 0 0 0 0 0 0 0
## limegold 0 0 0 0 0 0 0 0 0 0 0
## limegreen 0 0 0 0 0 50 0 0 0 0 0
## limeorange 0 0 0 0 0 0 0 0 0 0 0
## limepink 0 0 0 0 0 0 0 0 0 0 0
## limepurple 0 0 296 0 0 0 0 0 0 0 0
## limepurpleyellow 0 0 0 0 0 0 58 0 0 0 0
## limered 0 0 0 0 0 0 0 0 0 0 0
## limesilver 0 3 0 0 0 0 0 0 0 0 0
## limewhite 0 0 0 0 0 0 0 101 0 0 0
## limeyellow 0 0 0 0 52 0 0 0 0 0 0
## orange 0 0 0 0 0 0 0 0 0 0 0
## orangeblue 0 0 0 0 0 0 0 0 0 0 0
## orangegreen 0 0 0 0 0 0 0 0 0 0 0
## orangepink 0 0 0 0 0 0 0 0 0 0 50
## orangepurple 0 0 0 0 0 0 0 0 0 0 0
## purple 0 0 0 0 0 0 0 0 0 0 0
## redblue 0 0 0 0 0 0 673 0 0 0 0
## redgreen 0 0 0 0 0 0 0 0 0 0 0
## redpink 0 0 0 0 0 0 0 0 0 0 0
## redpurple 0 0 0 0 0 0 0 0 0 0 0
## silver 0 0 0 0 0 0 0 0 0 0 0
## white 0 0 0 0 0 0 0 0 0 0 0
## whiteblue 0 0 0 0 0 0 0 0 0 0 0
## whitegold 0 0 0 0 0 0 0 0 0 0 0
## whitegreen 0 0 0 0 0 0 0 0 0 0 0
## whiteorange 0 0 0 0 0 0 0 0 0 0 0
## whitepink 0 0 0 0 0 0 0 0 0 0 0
## whitepurple 0 0 0 0 0 0 0 0 0 0 0
## whitered 0 0 0 0 0 0 0 0 0 0 0
## whiteyellow 0 0 0 0 0 0 0 0 0 0 0
## yellowblue 0 0 0 0 0 0 0 0 0 0 0
## yellowgreen 0 0 0 0 0 0 0 0 0 0 0
## yelloworange 0 0 0 0 0 0 0 0 0 0 0
## yellowpink 0 0 0 0 0 0 0 0 0 2309 0
## yellowpurple 0 0 0 0 0 0 0 0 1243 0 0
## yellowred 0 0 0 0 0 0 0 0 0 0 0
## sl$IT_imputed
## sl$beeCol 3.98 3.99 4 4.02 4.03 4.05 4.07 4.12 4.13 4.14 4.15
## blue 0 0 0 0 0 0 0 0 0 0 0
## gold 0 0 0 0 0 54 0 0 0 0 0
## goldred 0 0 0 0 0 0 0 0 0 0 0
## green 0 0 0 0 0 0 0 0 0 0 0
## lime 0 0 0 28 0 0 0 0 0 0 0
## limeblue 0 0 0 0 0 0 0 0 0 0 0
## limegold 0 0 0 0 0 0 0 0 0 0 0
## limegreen 0 0 0 0 0 0 0 0 0 0 0
## limeorange 84 0 0 0 0 0 0 0 0 0 0
## limepink 0 0 0 0 0 0 0 0 51 0 0
## limepurple 0 0 0 0 0 0 0 0 0 0 0
## limepurpleyellow 0 0 0 0 0 0 0 0 0 0 0
## limered 0 0 0 0 0 0 0 0 0 0 3474
## limesilver 0 0 0 0 0 0 0 0 0 0 0
## limewhite 0 0 0 0 0 0 0 0 0 0 0
## limeyellow 0 0 0 0 0 0 0 0 0 0 0
## orange 0 501 0 0 0 0 0 0 0 0 0
## orangeblue 0 0 0 0 0 0 0 0 0 0 0
## orangegreen 0 0 0 0 0 0 0 0 0 0 0
## orangepink 0 0 0 0 0 0 0 0 0 0 0
## orangepurple 0 0 0 50 0 0 0 0 0 0 0
## purple 0 9 0 0 0 0 0 0 0 0 0
## redblue 0 0 0 0 0 0 0 0 0 0 0
## redgreen 0 0 0 0 0 0 0 530 0 0 0
## redpink 0 0 0 0 0 0 0 0 0 0 0
## redpurple 0 0 0 0 0 0 0 0 0 0 0
## silver 0 0 0 0 0 0 0 0 0 0 0
## white 0 0 0 0 283 0 0 0 0 0 0
## whiteblue 0 0 0 0 0 0 0 0 0 0 0
## whitegold 0 0 0 0 0 0 0 0 0 0 0
## whitegreen 0 0 39 0 0 0 0 0 0 0 0
## whiteorange 0 0 0 0 0 0 0 0 0 0 0
## whitepink 0 0 0 0 0 0 0 0 0 0 0
## whitepurple 0 0 0 0 0 0 0 0 0 0 0
## whitered 0 0 0 0 0 0 0 0 0 0 0
## whiteyellow 0 0 0 0 0 0 0 0 0 0 0
## yellowblue 0 0 0 0 0 0 0 0 0 0 0
## yellowgreen 0 0 0 0 0 0 0 0 0 532 0
## yelloworange 0 0 0 0 0 0 0 0 0 0 0
## yellowpink 0 0 0 0 0 0 0 0 0 0 0
## yellowpurple 0 0 0 0 0 0 0 0 0 0 0
## yellowred 0 0 0 0 0 0 547 0 0 0 0
## sl$IT_imputed
## sl$beeCol 4.21 4.23 4.24 4.31 4.34 4.37 4.42 4.45 4.46 4.59 4.61
## blue 0 0 0 0 0 0 0 0 0 0 0
## gold 0 0 0 0 0 0 0 0 0 0 0
## goldred 0 0 0 0 0 0 0 0 0 0 0
## green 13 0 0 0 0 0 0 0 0 0 0
## lime 0 0 0 0 0 0 0 0 0 0 0
## limeblue 0 0 0 0 0 0 0 0 0 0 0
## limegold 0 0 0 54 0 0 0 0 0 0 0
## limegreen 0 0 0 0 0 0 0 0 0 0 0
## limeorange 0 0 0 0 0 0 0 0 0 0 0
## limepink 0 0 0 0 0 0 0 0 0 0 0
## limepurple 0 0 0 0 0 0 0 0 0 0 0
## limepurpleyellow 0 0 0 0 0 0 0 0 0 0 0
## limered 0 0 0 0 0 0 0 0 0 0 0
## limesilver 0 0 0 0 0 0 0 0 0 0 0
## limewhite 0 0 0 0 0 0 0 0 0 0 0
## limeyellow 0 0 0 0 0 0 0 0 0 0 0
## orange 0 0 0 0 0 0 0 0 0 0 0
## orangeblue 0 0 0 0 0 0 0 0 0 0 50
## orangegreen 0 0 0 0 0 50 0 0 0 0 0
## orangepink 0 0 0 0 0 0 0 0 0 0 0
## orangepurple 0 0 0 0 0 0 0 0 0 0 0
## purple 0 0 0 0 0 0 0 0 0 0 0
## redblue 0 0 0 0 0 0 0 0 0 0 0
## redgreen 0 0 0 0 0 0 0 0 0 0 0
## redpink 677 0 0 0 0 0 0 0 0 0 0
## redpurple 0 0 0 0 218 0 0 0 0 0 0
## silver 0 26 0 0 0 0 0 0 0 0 0
## white 0 0 0 0 0 0 0 0 0 0 0
## whiteblue 0 0 0 0 2786 0 0 0 0 0 0
## whitegold 0 0 0 0 0 0 0 0 0 0 0
## whitegreen 0 0 0 0 0 0 0 0 0 0 0
## whiteorange 0 0 0 0 0 0 0 0 1122 0 0
## whitepink 0 0 0 0 0 0 0 0 0 1113 0
## whitepurple 0 0 0 0 0 0 0 55 0 0 0
## whitered 0 0 0 0 0 0 1344 0 0 0 0
## whiteyellow 0 0 157 0 0 0 0 0 0 0 0
## yellowblue 0 0 0 0 0 0 0 0 0 0 0
## yellowgreen 0 0 0 0 0 0 0 0 0 0 0
## yelloworange 0 0 0 0 2113 0 0 0 0 0 0
## yellowpink 0 0 0 0 0 0 0 0 0 0 0
## yellowpurple 0 0 0 0 0 0 0 0 0 0 0
## yellowred 0 0 0 0 0 0 0 0 0 0 0
## sl$IT_imputed
## sl$beeCol 4.63 4.69 4.94
## blue 0 0 0
## gold 0 0 0
## goldred 0 0 0
## green 0 0 0
## lime 0 0 0
## limeblue 0 530 0
## limegold 0 0 0
## limegreen 0 0 0
## limeorange 0 0 0
## limepink 0 0 0
## limepurple 0 0 0
## limepurpleyellow 0 0 0
## limered 0 0 0
## limesilver 0 0 0
## limewhite 0 0 0
## limeyellow 0 0 0
## orange 0 0 0
## orangeblue 0 0 0
## orangegreen 0 0 0
## orangepink 0 0 0
## orangepurple 0 0 0
## purple 0 0 0
## redblue 0 0 0
## redgreen 0 0 0
## redpink 0 0 0
## redpurple 0 0 0
## silver 0 0 0
## white 0 0 0
## whiteblue 0 0 0
## whitegold 1225 0 0
## whitegreen 0 0 0
## whiteorange 0 0 0
## whitepink 0 0 0
## whitepurple 0 0 0
## whitered 0 0 0
## whiteyellow 0 0 0
## yellowblue 0 0 1587
## yellowgreen 0 0 0
## yelloworange 0 0 0
## yellowpink 0 0 0
## yellowpurple 0 0 0
## yellowred 0 0 0# hist(unique(sl$IT_imputed[sl$trt2 == "low"]))
# rug(unique(sl$IT_imputed[sl$trt2 == "low"]))
#
# hist(unique(sl$IT_imputed[sl$trt2 == "high"]))
# rug(unique(sl$IT_imputed[sl$trt2 == "high"]))
# number of trials per bee and treatment
xtabs(~sl$beeCol + sl$trialNum)## sl$trialNum
## sl$beeCol 1 2 3 4 5 6 7 8 9 10 11 12
## blue 36 0 0 0 0 0 0 0 0 0 0 0
## gold 54 0 0 0 0 0 0 0 0 0 0 0
## goldred 3 0 0 0 0 0 0 0 0 0 0 0
## green 9 4 0 0 0 0 0 0 0 0 0 0
## lime 28 0 0 0 0 0 0 0 0 0 0 0
## limeblue 50 54 50 50 53 55 52 62 53 51 0 0
## limegold 54 0 0 0 0 0 0 0 0 0 0 0
## limegreen 50 0 0 0 0 0 0 0 0 0 0 0
## limeorange 33 51 0 0 0 0 0 0 0 0 0 0
## limepink 51 0 0 0 0 0 0 0 0 0 0 0
## limepurple 53 243 0 0 0 0 0 0 0 0 0 0
## limepurpleyellow 58 0 0 0 0 0 0 0 0 0 0 0
## limered 50 281 351 298 486 537 393 170 96 105 707 0
## limesilver 3 0 0 0 0 0 0 0 0 0 0 0
## limewhite 50 51 0 0 0 0 0 0 0 0 0 0
## limeyellow 52 0 0 0 0 0 0 0 0 0 0 0
## orange 85 0 11 88 14 85 137 81 0 0 0 0
## orangeblue 50 0 0 0 0 0 0 0 0 0 0 0
## orangegreen 50 0 0 0 0 0 0 0 0 0 0 0
## orangepink 50 0 0 0 0 0 0 0 0 0 0 0
## orangepurple 50 0 0 0 0 0 0 0 0 0 0 0
## purple 9 0 0 0 0 0 0 0 0 0 0 0
## redblue 49 60 67 131 72 50 60 55 33 49 47 0
## redgreen 51 53 51 53 51 60 54 54 52 51 0 0
## redpink 56 58 71 57 62 62 76 119 62 54 0 0
## redpurple 55 90 73 0 0 0 0 0 0 0 0 0
## silver 26 0 0 0 0 0 0 0 0 0 0 0
## white 48 67 46 40 57 25 0 0 0 0 0 0
## whiteblue 56 314 307 190 539 537 105 103 276 111 248 0
## whitegold 48 66 76 174 179 198 216 135 64 69 0 0
## whitegreen 39 0 0 0 0 0 0 0 0 0 0 0
## whiteorange 54 260 432 376 0 0 0 0 0 0 0 0
## whitepink 64 143 0 99 78 165 122 127 105 118 92 0
## whitepurple 55 0 0 0 0 0 0 0 0 0 0 0
## whitered 59 195 169 278 109 89 112 87 138 108 0 0
## whiteyellow 55 47 55 0 0 0 0 0 0 0 0 0
## yellowblue 54 128 92 114 149 116 251 198 167 118 200 0
## yellowgreen 51 50 56 50 58 52 56 51 54 54 0 0
## yelloworange 54 176 205 475 113 256 111 141 252 110 220 0
## yellowpink 58 142 103 140 235 187 278 111 366 266 372 51
## yellowpurple 54 346 360 330 153 0 0 0 0 0 0 0
## yellowred 57 56 58 55 51 57 55 56 50 52 0 0# tabulate num bees in each trt
tdf <- data.frame(xtabs(~sl$beeCol + sl$trt))
tdf2 <- acast(tdf, sl.beeCol~sl.trt, value.var="Freq")
tdf3 <- as.data.frame(tdf2 > 0)
trtdf<-table(interaction(tdf3$full, tdf3$high, tdf3$low))
names(trtdf) <- c("FULL ONLY", "FULL-HIGH", "FULL-LOW", "NA")
trtdf## FULL ONLY FULL-HIGH FULL-LOW NA
## 28 6 8 0# get number of buzzes in each treatment
colSums(tdf2)## full high low
## 4968 6505 12703# get the number of bees for each treatment (every bee has "full" treatment)
apply(tdf2, MARGIN = 2, function(x) length(x[x>0]))## full high low
## 42 6 8head(sl)## # A tibble: 6 x 29
## beeCol hive meanFreq IT_imputed index freq amp datetime rewNum rewTF
## <chr> <fct> <dbl> <dbl> <int> <dbl> <dbl> <chr> <int> <lgl>
## 1 blue 4 395. 3.61 1 400 0.0633 2016_11… 1 TRUE
## 2 blue 4 395. 3.61 2 360 0.343 2016_11… 2 TRUE
## 3 blue 4 395. 3.61 3 430 0.441 2016_11… 3 TRUE
## 4 blue 4 395. 3.61 4 420 0.168 2016_11… 4 TRUE
## 5 blue 4 395. 3.61 5 420 0.153 2016_11… 5 TRUE
## 6 blue 4 395. 3.61 6 410 0.183 2016_11… 6 TRUE
## # ... with 19 more variables: lowRewAmp <int>, highrewAmp <int>,
## # BeeNumCol <chr>, accFile <chr>, trialNum <int>, datetime_str <dttm>,
## # lowFrq <int>, highFrq <int>, IT <dbl>, trt <fct>, amp_acc <dbl>,
## # timeSinceStart <lgl>, timeSinceLastBuzz <lgl>,
## # buzzesSinceReward <lgl>, beeColHive <fct>, colNum <chr>, trt2 <chr>,
## # amp_acc2 <dbl>, trialNum0 <dbl>dim(sl) # should be 24176 rows## [1] 24176 29# hive 5 is most common
table(sl$hive, useNA = 'always')##
## 3 4 5 <NA>
## 564 1047 22565 0# make sure there are values lower than 220 and higher than 450
# (the cutoff for buzzes used in the experiment)
hist(sl$freq, breaks = seq(215, 450, by = 10))
nrow(sl[sl$freq < 220 | sl$freq > 450,]) # should have 0 rows## [1] 0# look at treatments
xtabs(~sl$beeCol+ trt, data = sl )## trt
## sl$beeCol full high low
## blue 36 0 0
## gold 54 0 0
## goldred 3 0 0
## green 13 0 0
## lime 28 0 0
## limeblue 530 0 0
## limegold 54 0 0
## limegreen 50 0 0
## limeorange 84 0 0
## limepink 51 0 0
## limepurple 53 0 243
## limepurpleyellow 58 0 0
## limered 50 0 3424
## limesilver 3 0 0
## limewhite 101 0 0
## limeyellow 52 0 0
## orange 85 416 0
## orangeblue 50 0 0
## orangegreen 50 0 0
## orangepink 50 0 0
## orangepurple 50 0 0
## purple 9 0 0
## redblue 673 0 0
## redgreen 530 0 0
## redpink 56 0 621
## redpurple 55 163 0
## silver 26 0 0
## white 283 0 0
## whiteblue 56 0 2730
## whitegold 48 0 1177
## whitegreen 39 0 0
## whiteorange 54 0 1068
## whitepink 64 1049 0
## whitepurple 55 0 0
## whitered 59 1285 0
## whiteyellow 157 0 0
## yellowblue 54 1533 0
## yellowgreen 532 0 0
## yelloworange 54 2059 0
## yellowpink 58 0 2251
## yellowpurple 54 0 1189
## yellowred 547 0 0# find percentage reward by treatment
mean(grepl("[tT]", as.character(sl$rewTF))) # overall mean## [1] 0.4124752# percentage that were rewarded by treatment
tapply((grepl("[tT]", as.character(sl$rewTF))), INDEX= sl$trt, mean)## full high low
## 1.0000000 0.3535742 0.2128631# std dev for rewards by treatment
s2 = sl %>%
mutate(beeColHive = interaction(beeCol, hive),
reward= grepl(pattern = "t", x = .$rewTF, ignore.case = TRUE)*1)
s3 = s2 %>%
group_by(beeColHive, trt2) %>%
dplyr::summarize(mean_reward = mean(reward)) %>%
group_by(trt2) %>%
dplyr::arrange(desc(trt2))
s3## # A tibble: 66 x 3
## # Groups: trt2 [4]
## beeColHive trt2 mean_reward
## <fct> <chr> <dbl>
## 1 limepurple.5 low 0.210
## 2 limered.5 low 0.118
## 3 redpink.5 low 0.791
## 4 whiteblue.5 low 0.187
## 5 whitegold.5 low 0.371
## 6 whiteorange.5 low 0.147
## 7 yellowpink.5 low 0.208
## 8 yellowpurple.5 low 0.156
## 9 orange.3 high 0.471
## 10 redpurple.5 high 0.552
## # ... with 56 more rows# summary for paper
s3 %>%
dplyr::summarize( mean_ = mean(mean_reward), reward_sd = sd(mean_reward), n = n()) %>%
mutate(se = reward_sd / sqrt(n))## # A tibble: 4 x 5
## trt2 mean_ reward_sd n se
## <chr> <dbl> <dbl> <int> <dbl>
## 1 full 1 0 42 0
## 2 full_2 1 0 10 0
## 3 high 0.409 0.105 6 0.0429
## 4 low 0.273 0.223 8 0.0787# total number of trials for each treatment
tapply((grepl("[tT]", as.character(sl$rewTF))), INDEX= sl$trt, length)## full high low
## 4968 6505 12703# total number of rewards per treatment
tapply((grepl("[tT]", as.character(sl$rewTF))), INDEX= sl$trt, FUN = function(x) sum(x))## full high low
## 4968 2300 2704# total number of trials that were unrewarded per treatment
tapply((grepl("[tT]", as.character(sl$rewTF))), INDEX= sl$trt, FUN = function(x) sum(!x))## full high low
## 0 4205 9999# calculate avg freq & SE
se <- function(x) {
sd(x) / sqrt(length(x))
}
s4 = s2 %>%
group_by(beeColHive, trt2) %>%
dplyr::summarize(mean_freq = mean(freq),
sd_freq = sd(freq)) %>%
group_by(trt2) %>%
dplyr::arrange(desc(trt2)) %>%
filter(trt2 != "full") %>%
ungroup() %>%
mutate(trt3 = plyr::mapvalues(.$trt2, from = c("full_2", "high", "low"),
to = c("Full range\n(220 - 450 Hz)",
"High range\n(340 - 390 Hz)",
"Low range\n(220 - 330 Hz)")))
# plot bee size in the groups
sl_sum <- sl %>%
dplyr::select(beeColHive, trt2, IT_imputed) %>%
distinct(beeColHive, trt2, .keep_all = TRUE)
sl_sum %>% print(n = 50)## # A tibble: 66 x 3
## beeColHive trt2 IT_imputed
## <fct> <chr> <dbl>
## 1 blue.4 full 3.61
## 2 gold.3 full 4.05
## 3 goldred.4 full 3.01
## 4 green.4 full 4.21
## 5 green.4 full_2 4.21
## 6 lime.5 full 4.02
## 7 limeblue.5 full_2 4.69
## 8 limeblue.5 full 4.69
## 9 limegold.5 full 4.31
## 10 limegreen.5 full 3.74
## 11 limeorange.5 full_2 3.98
## 12 limeorange.5 full 3.98
## 13 limepink.5 full 4.13
## 14 limepurple.5 low 3.48
## 15 limepurple.5 full 3.48
## 16 limepurpleyellow.5 full 3.75
## 17 limered.5 low 4.15
## 18 limered.5 full 4.15
## 19 limesilver.5 full 3.4
## 20 limewhite.5 full_2 3.76
## 21 limewhite.5 full 3.76
## 22 limeyellow.5 full 3.67
## 23 orange.3 high 3.99
## 24 orange.3 full 3.99
## 25 orangeblue.5 full 4.61
## 26 orangegreen.5 full 4.37
## 27 orangepink.5 full 3.96
## 28 orangepurple.5 full 4.02
## 29 purple.3 full 3.99
## 30 redblue.4 full_2 3.75
## 31 redblue.4 full 3.75
## 32 redgreen.5 full_2 4.12
## 33 redgreen.5 full 4.12
## 34 redpink.5 low 4.21
## 35 redpink.5 full 4.21
## 36 redpurple.5 full 4.34
## 37 redpurple.5 high 4.34
## 38 silver.5 full 4.23
## 39 white.4 full 4.03
## 40 white.4 full_2 4.03
## 41 whiteblue.5 low 4.34
## 42 whiteblue.5 full 4.34
## 43 whitegold.5 full 4.63
## 44 whitegold.5 low 4.63
## 45 whitegreen.4 full 4
## 46 whiteorange.5 low 4.46
## 47 whiteorange.5 full 4.46
## 48 whitepink.5 high 4.59
## 49 whitepink.5 full 4.59
## 50 whitepurple.5 full 4.45
## # ... with 16 more rowssl_sum %>%
group_by(trt2) %>%
summarize(counts = n())## # A tibble: 4 x 2
## trt2 counts
## <chr> <int>
## 1 full 42
## 2 full_2 10
## 3 high 6
## 4 low 8sl %>% nrow## [1] 24176sl %>% group_by(beeColHive) %>%
summarize(n()) %>%
nrow()## [1] 42print(unique(sl$beeColHive))## [1] blue.4 gold.3 goldred.4
## [4] green.4 lime.5 limeblue.5
## [7] limegold.5 limegreen.5 limeorange.5
## [10] limepink.5 limepurple.5 limepurpleyellow.5
## [13] limered.5 limesilver.5 limewhite.5
## [16] limeyellow.5 orange.3 orangeblue.5
## [19] orangegreen.5 orangepink.5 orangepurple.5
## [22] purple.3 redblue.4 redgreen.5
## [25] redpink.5 redpurple.5 silver.5
## [28] white.4 whiteblue.5 whitegold.5
## [31] whitegreen.4 whiteorange.5 whitepink.5
## [34] whitepurple.5 whitered.5 whiteyellow.5
## [37] yellowblue.5 yellowgreen.5 yelloworange.5
## [40] yellowpink.5 yellowpurple.5 yellowred.5
## 126 Levels: blue.3 gold.3 goldred.3 green.3 lime.3 ... yellowred.5print(unique(sl$beeCol))## [1] "blue" "gold" "goldred"
## [4] "green" "lime" "limeblue"
## [7] "limegold" "limegreen" "limeorange"
## [10] "limepink" "limepurple" "limepurpleyellow"
## [13] "limered" "limesilver" "limewhite"
## [16] "limeyellow" "orange" "orangeblue"
## [19] "orangegreen" "orangepink" "orangepurple"
## [22] "purple" "redblue" "redgreen"
## [25] "redpink" "redpurple" "silver"
## [28] "white" "whiteblue" "whitegold"
## [31] "whitegreen" "whiteorange" "whitepink"
## [34] "whitepurple" "whitered" "whiteyellow"
## [37] "yellowblue" "yellowgreen" "yelloworange"
## [40] "yellowpink" "yellowpurple" "yellowred"sl %>% group_by(trt2) %>%
summarize(count= n())## # A tibble: 4 x 2
## trt2 count
## <chr> <int>
## 1 full 1971
## 2 full_2 2997
## 3 high 6505
## 4 low 12703sl_sum %>% group_by(trt2) %>%
summarize(count= n())## # A tibble: 4 x 2
## trt2 count
## <chr> <int>
## 1 full 42
## 2 full_2 10
## 3 high 6
## 4 low 8s2 <- sl_sum %>%
filter(trt2 != "full")
s2 <- s2 %>%
mutate(trt3 = plyr::mapvalues(.$trt2, from = c("full_2", "high", "low"),
to = c("Full range\n(220 - 450 Hz)",
"High range\n(340 - 390 Hz)",
"Low range\n(220 - 330 Hz)")))
s44 <- full_join(s2, s4)## Joining, by = c("beeColHive", "trt2", "trt3")s44## # A tibble: 24 x 6
## beeColHive trt2 IT_imputed trt3 mean_freq sd_freq
## <fct> <chr> <dbl> <chr> <dbl> <dbl>
## 1 green.4 full_2 4.21 "Full range\n(220 - 4… 285 59.2
## 2 limeblue.5 full_2 4.69 "Full range\n(220 - 4… 304. 40.1
## 3 limeorange.5 full_2 3.98 "Full range\n(220 - 4… 345. 26.7
## 4 limepurple.5 low 3.48 "Low range\n(220 - 33… 344. 32.6
## 5 limered.5 low 4.15 "Low range\n(220 - 33… 365. 37.1
## 6 limewhite.5 full_2 3.76 "Full range\n(220 - 4… 327. 35.5
## 7 orange.3 high 3.99 "High range\n(340 - 3… 382. 27.5
## 8 redblue.4 full_2 3.75 "Full range\n(220 - 4… 341. 43.3
## 9 redgreen.5 full_2 4.12 "Full range\n(220 - 4… 326. 36.4
## 10 redpink.5 low 4.21 "Low range\n(220 - 33… 290. 39.1
## # ... with 14 more rowsset.seed(123)
ggplot(s44, aes(x = trt3, y= IT_imputed)) +
geom_violin(width = 0.5, fill = "grey90", color = NA, bw = 0.2, trim = TRUE) +
#geom_boxplot(width = 0.5, outlier.alpha = 0) +
geom_point(position = position_jitter(height = 0, width = 0.05),
stroke= 0, size = 2.5, color = 'grey40') +
labs(x = "Frequency range for reward", y = "Intertegular span (mm)") 
ggsave(file.path(figDir, "beeSize_exp1.png"), width = 6, height = 4, units = "in", dpi = 500)
ggsave(file.path(figDir, "beeSize_exp1.svg"), width = 6, height = 4, units = "in")
# plot freq vs. amp
sl <- sl %>%
mutate(`IT interval (mm)` = cut_interval(IT_imputed, n = 3))
ggp <- ggplot(sl[sl$trt2 == "full", ], aes(x = freq, y = amp_acc2)) +
geom_point(position = position_jitter(height = 0, width = 2),
alpha = 0.3, stroke = 0, size = 1.5) +
stat_smooth(method = 'loess', span = 1, color = 'grey40', se = FALSE,
lwd = 1.3) +
facet_wrap(~`IT interval (mm)`, labeller = "label_both") +
theme(strip.background = element_blank(),
strip.text = element_text(size = 10)) +
theme(legend.position = "none",
plot.margin = unit(c(1,1,1,1)/2, "cm")) +
labs(x = "Sonication Frequency (Hz)", y = expression ("Sonication acceleration "(m~s^{-2})))
ggp
ggsave(file.path(figDir, "freq_amp.png"), width = 6.5, height = 3, units = "in", dpi = 500)
svg(file.path(figDir, "freq_amp.svg"), width = 6.5, height = 3)
ggp
dev.off()## quartz_off_screen
## 2MODELING
# summary for paper
# fit a varying slope and intercept for colNum (bee ID), and allow the slope of the trialNum
# variable to vary by colNum (beeID)
# basically the same model
sl$trialNum## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [23] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [45] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [67] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [89] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1
## [111] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [133] 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [155] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [177] 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 10 10 10 10 10 10
## [199] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [221] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 1 1 1 1
## [243] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [265] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [287] 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [309] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [331] 4 4 4 4 4 4 4 4 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [353] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [375] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [397] 8 8 8 8 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [419] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [441] 3 3 3 3 3 3 3 3 3 3 7 7 7 7 7 7 7 7 7 7 7 7
## [463] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [485] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 2 2 2 2
## [507] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [529] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [551] 2 2 2 2 2 2 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [573] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [595] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 6 6 6 6 6 6
## [617] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [639] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [661] 6 6 6 6 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [683] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [705] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [727] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [749] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
## [771] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [793] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [837] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [859] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [881] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [903] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [925] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [947] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [969] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [991] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1013] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1035] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1057] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1079] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1101] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1123] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [1145] 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1167] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1189] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1211] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1233] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1255] 1 1 1 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1277] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1299] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1321] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1343] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1365] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1387] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1409] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1431] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1453] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1475] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1497] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1519] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1541] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1563] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1585] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1607] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1629] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1651] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1673] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1695] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1717] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1739] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1761] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1783] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1805] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1827] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1849] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1871] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1893] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1915] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1937] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [1959] 11 11 11 11 11 11 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [1981] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [2003] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [2025] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [2047] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 5 5 5 5 5 5 5 5
## [2069] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2091] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2113] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2135] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2157] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2179] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2201] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2223] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2245] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2267] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2289] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2311] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2333] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2355] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2377] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2399] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2421] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2443] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2465] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2487] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2509] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [2531] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6
## [2553] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2575] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2597] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2619] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2641] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2663] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2685] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2707] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2729] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2751] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2773] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2795] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2817] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2839] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2861] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2883] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2905] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2927] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2949] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2971] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [2993] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [3015] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [3037] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [3059] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [3081] 6 6 6 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3103] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3125] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3147] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3169] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3191] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3213] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3235] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3257] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3279] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3301] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3323] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3345] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1
## [3367] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3389] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [3411] 1 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3433] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3455] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3477] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3499] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3521] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3543] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3565] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3587] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3609] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3631] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3653] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3675] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3697] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 10 10 10 10 10 10
## [3719] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [3741] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [3763] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [3785] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [3807] 10 10 10 10 10 10 10 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8
## [3829] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [3851] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [3873] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [3895] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [3917] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [3939] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [3961] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [3983] 8 8 8 8 8 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4005] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4027] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4049] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4071] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4093] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4115] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4137] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4159] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4181] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4203] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4225] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4247] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4269] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4291] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4313] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [4335] 3 3 3 3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4357] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4379] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4401] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4423] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4445] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4467] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4489] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4511] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4533] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4555] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4577] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4599] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4621] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4643] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4665] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4687] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4709] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [4731] 7 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [4753] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [4775] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1
## [4797] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4819] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4841] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4863] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [4885] 1 1 1 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [4907] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [4929] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [4951] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 6
## [4973] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [4995] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [5017] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [5039] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5
## [5061] 5 5 5 5 5 5 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5083] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5105] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5127] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5149] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5171] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5193] 7 7 7 7 7 7 7 7 7 7 7 7 3 3 3 3 3 3 3 3 3 3
## [5215] 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5237] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5259] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5281] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5303] 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5325] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5347] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5369] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5391] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5413] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5435] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5457] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5479] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5501] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5523] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5545] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5567] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5589] 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6 6 6 6 6 6
## [5611] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [5633] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 1 1 1 1 1 1 1
## [5655] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [5677] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 9 9
## [5699] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [5721] 9 9 9 9 9 9 9 9 9 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5743] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5765] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5787] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5809] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5831] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [5853] 4 4 4 4 4 4 4 4 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5875] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5897] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [5919] 7 7 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [5941] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [5963] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [5985] 5 5 5 5 5 5 5 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [6007] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [6029] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 3 3 3
## [6051] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6073] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6095] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 10 10
## [6117] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [6139] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [6161] 10 10 10 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [6183] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [6205] 11 11 11 11 11 11 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [6227] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [6249] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [6271] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [6293] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [6315] 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [6337] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [6359] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1
## [6381] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6403] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [6425] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [6447] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [6469] 2 2 2 2 2 2 2 2 2 2 9 9 9 9 9 9 9 9 9 9 9 9
## [6491] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [6513] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6 6 6 6
## [6535] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [6557] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [6579] 6 6 6 6 6 6 6 6 6 6 6 6 8 8 8 8 8 8 8 8 8 8
## [6601] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [6623] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [6645] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [6667] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [6689] 10 10 10 10 10 10 10 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6711] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [6733] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 7 7 7 7 7 7 7 7
## [6755] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [6777] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [6799] 7 7 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [6821] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [6843] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 10 10 10 10 10 10
## [6865] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [6887] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [6909] 10 10 10 10 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [6931] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [6953] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [6975] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [6997] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [7019] 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7
## [7041] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [7063] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [7085] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [7107] 7 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [7129] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [7151] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3
## [7173] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7195] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7217] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7239] 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [7261] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [7283] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4 4 4 4
## [7305] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [7327] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [7349] 4 4 4 4 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [7371] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [7393] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [7415] 6 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [7437] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [7459] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1
## [7481] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [7503] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [7525] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [7547] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [7569] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [7591] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [7613] 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3
## [7635] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7657] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7679] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1
## [7701] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [7723] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [7745] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [7767] 1 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [7789] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6
## [7811] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [7833] 6 6 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7855] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [7877] 3 3 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [7899] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [7921] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 2 2 2 2 2
## [7943] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [7965] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [7987] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7
## [8009] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [8031] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [8053] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [8075] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [8097] 7 7 7 7 7 7 7 7 7 7 7 7 7 3 3 3 3 3 3 3 3 3
## [8119] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8141] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8163] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8185] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8207] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8229] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8251] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8273] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8295] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8317] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8339] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8361] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8383] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [8405] 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4
## [8427] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [8449] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [8471] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [8493] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [8515] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [8537] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [8559] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [8581] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [8603] 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [8625] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [8647] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 8 8 8 8 8 8
## [8669] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [8691] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [8713] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [8735] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [8757] 8 8 8 8 8 8 8 8 8 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8779] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8801] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8823] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8845] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8867] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8889] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8911] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8933] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8955] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8977] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [8999] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 6 6 6 6 6 6 6
## [9021] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9043] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9065] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9087] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9109] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9131] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9153] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9175] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9197] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9219] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9241] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9263] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9285] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9307] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9329] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9351] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9373] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9395] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9417] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9439] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9461] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9483] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9505] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9527] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [9549] 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9571] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9593] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9615] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9637] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9659] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9681] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9703] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9725] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9747] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9769] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9791] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9813] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9835] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9857] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9879] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9901] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9923] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9945] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9967] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [9989] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [10011] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [10033] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [10055] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [10077] 5 5 5 5 5 5 5 5 5 5 5 5 5 9 9 9 9 9 9 9 9 9
## [10099] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10121] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10143] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10165] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10187] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10209] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10231] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10253] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10275] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10297] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10319] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10341] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [10363] 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [10385] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [10407] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [10429] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [10451] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [10473] 10 10 10 10 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10495] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10517] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10539] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10561] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10583] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10605] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10627] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10649] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10671] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10693] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10715] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10737] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10759] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [10781] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
## [10803] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [10825] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 6
## [10847] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [10869] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [10891] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [10913] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [10935] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [10957] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [10979] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [11001] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [11023] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 9 9 9 9 9 9
## [11045] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [11067] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [11089] 9 9 9 9 9 9 9 9 9 9 9 9 5 5 5 5 5 5 5 5 5 5
## [11111] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [11133] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [11155] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [11177] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [11199] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [11221] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [11243] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [11265] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 7 7 7 7 7 7 7
## [11287] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11309] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11331] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11353] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11375] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11397] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11419] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11441] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11463] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [11485] 7 7 7 7 7 7 7 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4
## [11507] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [11529] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [11551] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [11573] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [11595] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [11617] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [11639] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [11661] 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11683] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11705] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [11727] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 10 10 10
## [11749] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [11771] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [11793] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [11815] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [11837] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [11859] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [11881] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [11903] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [11925] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [11947] 8 8 8 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [11969] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [11991] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12013] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [12035] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
## [12057] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12079] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12101] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12123] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12145] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12167] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12189] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12211] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12233] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12255] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12277] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [12299] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1
## [12321] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [12343] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [12365] 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12387] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12409] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12431] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12453] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12475] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12497] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12519] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12541] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12563] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12585] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12607] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12629] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12651] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12673] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12695] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12717] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12739] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12761] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [12783] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4
## [12805] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [12827] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [12849] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [12871] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [12893] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [12915] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [12937] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [12959] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [12981] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13003] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13025] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13047] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13069] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13091] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13113] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13135] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13157] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6
## [13179] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [13201] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [13223] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [13245] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [13267] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [13289] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [13311] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [13333] 6 6 6 6 6 6 6 6 6 2 2 2 2 2 2 2 2 2 2 2 2 2
## [13355] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [13377] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [13399] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [13421] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [13443] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [13465] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 10 10
## [13487] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [13509] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [13531] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [13553] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [13575] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [13597] 10 10 10 10 10 10 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [13619] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [13641] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [13663] 1 1 1 1 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [13685] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [13707] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [13729] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [13751] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [13773] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 4 4 4 4 4 4
## [13795] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13817] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13839] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13861] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [13883] 4 4 4 4 4 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [13905] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [13927] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [13949] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [13971] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [13993] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [14015] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [14037] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [14059] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [14081] 11 11 11 11 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [14103] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [14125] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [14147] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [14169] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [14191] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5
## [14213] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [14235] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [14257] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [14279] 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1
## [14301] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [14323] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [14345] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [14367] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [14389] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [14411] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5
## [14433] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [14455] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [14477] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [14499] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [14521] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 9 9
## [14543] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [14565] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [14587] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [14609] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [14631] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [14653] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [14675] 9 9 9 9 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [14697] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [14719] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4
## [14741] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14763] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14785] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14807] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14829] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14851] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14873] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14895] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14917] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14939] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14961] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [14983] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [15005] 4 4 4 4 4 4 4 4 4 4 4 10 10 10 10 10 10 10 10 10 10 10
## [15027] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15049] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15071] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15093] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15115] 10 10 10 10 10 10 10 10 10 6 6 6 6 6 6 6 6 6 6 6 6 6
## [15137] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [15159] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [15181] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [15203] 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7
## [15225] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [15247] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [15269] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [15291] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [15313] 7 7 7 7 7 7 7 7 7 7 7 7 3 3 3 3 3 3 3 3 3 3
## [15335] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15357] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15379] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15401] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15423] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15445] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15467] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15489] 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15511] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15533] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15555] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15577] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15599] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15621] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15643] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15665] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15687] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15709] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [15731] 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [15753] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [15775] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3
## [15797] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15819] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [15841] 3 3 3 3 3 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15863] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15885] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15907] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15929] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [15951] 10 10 10 10 10 10 10 10 10 10 10 10 10 6 6 6 6 6 6 6 6 6
## [15973] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [15995] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [16017] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [16039] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [16061] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 8 8 8
## [16083] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [16105] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [16127] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [16149] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [16171] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [16193] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [16215] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [16237] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [16259] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4
## [16281] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [16303] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [16325] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [16347] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [16369] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [16391] 4 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [16413] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [16435] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [16457] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [16479] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [16501] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [16523] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [16545] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 2 2 2 2 2 2 2 2
## [16567] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [16589] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [16611] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [16633] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [16655] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [16677] 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5
## [16699] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [16721] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [16743] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [16765] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [16787] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [16809] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [16831] 5 5 5 5 5 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [16853] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [16875] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [16897] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [16919] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [16941] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [16963] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [16985] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [17007] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [17029] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [17051] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [17073] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 1 1 1 1 1 1 1 1
## [17095] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [17117] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [17139] 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17161] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17183] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17205] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17227] 3 3 3 3 3 3 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17249] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17271] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17293] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17315] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17337] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17359] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17381] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17403] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [17425] 11 11 11 11 11 11 11 11 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [17447] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [17469] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 6 6 6 6 6 6 6 6
## [17491] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [17513] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [17535] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [17557] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [17579] 10 10 10 10 10 10 10 10 10 10 4 4 4 4 4 4 4 4 4 4 4 4
## [17601] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [17623] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 7 7 7 7 7 7
## [17645] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [17667] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [17689] 7 7 7 7 7 7 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [17711] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [17733] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3
## [17755] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17777] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [17799] 3 3 3 3 3 3 3 3 3 3 8 8 8 8 8 8 8 8 8 8 8 8
## [17821] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [17843] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 1 1 1 1 1
## [17865] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [17887] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [17909] 1 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [17931] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [17953] 9 9 9 9 9 9 9 9 9 9 9 9 1 1 1 1 1 1 1 1 1 1
## [17975] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [17997] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [18019] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [18041] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [18063] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [18085] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [18107] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [18129] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18151] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18173] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18195] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18217] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18239] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18261] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18283] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18305] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18327] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18349] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [18371] 9 9 9 9 9 9 9 9 9 9 8 8 8 8 8 8 8 8 8 8 8 8
## [18393] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [18415] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [18437] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [18459] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [18481] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [18503] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 11 11 11
## [18525] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18547] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18569] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18591] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18613] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18635] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18657] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18679] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18701] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [18723] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 6 6 6
## [18745] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18767] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18789] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18811] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18833] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18855] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18877] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18899] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18921] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18943] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18965] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [18987] 6 6 6 6 6 6 6 6 6 6 6 3 3 3 3 3 3 3 3 3 3 3
## [19009] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [19031] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [19053] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [19075] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [19097] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [19119] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [19141] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [19163] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [19185] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5
## [19207] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [19229] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [19251] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [19273] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [19295] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4
## [19317] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19339] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19361] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19383] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19405] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19427] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19449] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19471] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19493] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19515] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19537] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19559] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19581] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19603] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19625] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19647] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19669] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19691] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19713] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19735] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19757] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [19779] 4 4 4 4 4 4 4 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2
## [19801] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [19823] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [19845] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [19867] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [19889] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [19911] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [19933] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [19955] 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7 7
## [19977] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [19999] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20021] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20043] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20065] 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8
## [20087] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [20109] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [20131] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [20153] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [20175] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7
## [20197] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20219] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20241] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20263] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20285] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20307] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20329] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20351] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20373] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20395] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20417] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20439] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [20461] 7 7 7 7 7 7 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [20483] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [20505] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [20527] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [20549] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [20571] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [20593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 10 10 10 10 10 10
## [20615] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20637] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20659] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20681] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20703] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20725] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20747] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20769] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20791] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20813] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20835] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [20857] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 4 4 4 4
## [20879] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [20901] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [20923] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [20945] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [20967] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [20989] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [21011] 4 4 4 4 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21033] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21055] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21077] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21099] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21121] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21143] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21165] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21187] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21209] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21231] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21253] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21275] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21297] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21319] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21341] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21363] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
## [21385] 11 11 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [21407] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [21429] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [21451] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [21473] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [21495] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [21517] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [21539] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [21561] 6 6 6 6 6 6 6 6 6 6 6 6 6 9 9 9 9 9 9 9 9 9
## [21583] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21605] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21627] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21649] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21671] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21693] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21715] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21737] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21759] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21781] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21803] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21825] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21847] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21869] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21891] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21913] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [21935] 9 9 9 9 9 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [21957] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [21979] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [22001] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [22023] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [22045] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [22067] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [22089] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [22111] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [22133] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [22155] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3
## [22177] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22199] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22221] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22243] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22265] 3 3 3 3 3 3 3 3 3 3 3 3 3 12 12 12 12 12 12 12 12 12
## [22287] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
## [22309] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 1 1
## [22331] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [22353] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [22375] 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4 4 4 4 4 4
## [22397] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22419] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22441] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22463] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22485] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22507] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22529] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22551] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22573] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22595] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22617] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22639] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22661] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22683] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [22705] 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3
## [22727] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22749] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22771] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22793] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22815] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22837] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22859] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22881] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22903] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22925] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22947] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22969] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [22991] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [23013] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [23035] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [23057] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5
## [23079] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [23101] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [23123] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [23145] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [23167] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [23189] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [23211] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 2 2 2
## [23233] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23255] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23277] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23299] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23321] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23343] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23365] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23387] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23409] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23431] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23453] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23475] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23497] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23541] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23563] 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1
## [23585] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [23607] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [23629] 1 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [23651] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
## [23673] 9 9 9 9 9 9 9 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [23695] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
## [23717] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 10 10 10 10 10 10 10 10
## [23739] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [23761] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
## [23783] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [23805] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
## [23827] 8 8 8 8 8 8 8 8 8 8 8 8 4 4 4 4 4 4 4 4 4 4
## [23849] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [23871] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [23893] 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23915] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [23937] 2 2 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7 7 7 7 7 7
## [23959] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [23981] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
## [24003] 7 7 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [24025] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## [24047] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 3 3 3 3 3 3 3
## [24069] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [24091] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [24113] 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [24135] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [24157] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1# treatment (rewarded at high, low, or full range of frequencies), IT span, colony, trial number, and the interaction of treatment and IT span
m2 = lmer(freq ~ trt + IT_imputed + hive + trialNum + trt:IT_imputed + (1+trialNum|beeColHive), data = sl, REML = FALSE)
summary(m2)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: freq ~ trt + IT_imputed + hive + trialNum + trt:IT_imputed +
## (1 + trialNum | beeColHive)
## Data: sl
##
## AIC BIC logLik deviance df.resid
## 244741.4 244846.6 -122357.7 244715.4 24163
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0041 -0.5147 0.1597 0.6814 4.0131
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beeColHive (Intercept) 907.5 30.12
## trialNum 102.1 10.10 -0.77
## Residual 1440.3 37.95
## Number of obs: 24176, groups: beeColHive, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 508.614 43.545 11.680
## trthigh 32.935 40.445 0.814
## trtlow -64.928 28.900 -2.247
## IT_imputed -37.023 10.405 -3.558
## hive4 -28.959 16.264 -1.781
## hive5 -34.911 13.817 -2.527
## trialNum 1.335 2.046 0.652
## trthigh:IT_imputed -4.019 8.990 -0.447
## trtlow:IT_imputed 20.454 6.915 2.958
##
## Correlation of Fixed Effects:
## (Intr) trthgh trtlow IT_mpt hive4 hive5 trilNm trth:IT_
## trthigh -0.080
## trtlow -0.180 0.014
## IT_imputed -0.950 0.064 0.195
## hive4 -0.350 0.064 0.023 0.111
## hive5 -0.163 0.061 -0.034 -0.129 0.759
## trialNum 0.027 -0.036 -0.095 -0.105 -0.025 -0.029
## trthgh:IT_m 0.084 -0.998 -0.015 -0.069 -0.063 -0.060 0.033
## trtlw:IT_mp 0.178 -0.014 -0.997 -0.193 -0.022 0.031 0.089 0.015# this model estimates a global intercept
# random effect intercept for colNum (beeID)
# a single global estimate for trialNum
# the effect of trialNum within each level of colNum
# the correlation between intercept of trialNum across levels of colNum
vif.mer <- function (fit) {
## adapted from rms::vif
v <- vcov(fit)
nam <- names(fixef(fit))
## exclude intercepts
ns <- sum(1 * (nam == "Intercept" | nam == "(Intercept)"))
if (ns > 0) {
v <- v[-(1:ns), -(1:ns), drop = FALSE]
nam <- nam[-(1:ns)]
}
d <- diag(v)^0.5
v <- diag(solve(v/(d %o% d)))
names(v) <- nam
v
}
vif.mer(m2) # fine## trthigh trtlow IT_imputed
## 276.592066 194.117209 1.201656
## hive4 hive5 trialNum
## 2.645079 2.667568 1.029930
## trthigh:IT_imputed trtlow:IT_imputed
## 276.722958 193.727604m3 = update(m2, .~. - hive)
BIC(m2, m3) # m3 is better (without hive)## df BIC
## m2 13 244846.6
## m3 11 244832.4anova(m2, m3) # Note: disagrees with BIC## Data: sl
## Models:
## m3: freq ~ trt + IT_imputed + trialNum + (1 + trialNum | beeColHive) +
## m3: trt:IT_imputed
## m2: freq ~ trt + IT_imputed + hive + trialNum + trt:IT_imputed +
## m2: (1 + trialNum | beeColHive)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m3 11 244743 244832 -122361 244721
## m2 13 244741 244847 -122358 244715 5.9991 2 0.04981 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m3)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## freq ~ trt + IT_imputed + trialNum + (1 + trialNum | beeColHive) +
## trt:IT_imputed
## Data: sl
##
## AIC BIC logLik deviance df.resid
## 244743.4 244832.4 -122360.7 244721.4 24165
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0026 -0.5121 0.1613 0.6812 4.0225
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beeColHive (Intercept) 1010.0 31.78
## trialNum 101.7 10.08 -0.76
## Residual 1440.4 37.95
## Number of obs: 24176, groups: beeColHive, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 494.843 42.810 11.559
## trthigh 40.207 40.456 0.994
## trtlow -67.847 28.867 -2.350
## IT_imputed -41.353 10.398 -3.977
## trialNum 1.222 2.040 0.599
## trthigh:IT_imputed -5.605 8.993 -0.623
## trtlow:IT_imputed 21.086 6.910 3.052
##
## Correlation of Fixed Effects:
## (Intr) trthgh trtlow IT_mpt trilNm trth:IT_
## trthigh -0.061
## trtlow -0.168 0.013
## IT_imputed -0.993 0.063 0.172
## trialNum 0.028 -0.034 -0.095 -0.117
## trthgh:IT_m 0.066 -0.998 -0.014 -0.068 0.031
## trtlw:IT_mp 0.166 -0.013 -0.997 -0.171 0.090 0.014m4 <- update(m3, .~. - IT_imputed:trt)
BIC(m3, m4) # m4 better## df BIC
## m3 11 244832.4
## m4 9 244820.8summary(m4)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: freq ~ trt + IT_imputed + trialNum + (1 + trialNum | beeColHive)
## Data: sl
##
## AIC BIC logLik deviance df.resid
## 244748.0 244820.8 -122365.0 244730.0 24167
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9968 -0.5117 0.1600 0.6805 4.0007
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beeColHive (Intercept) 1102.8 33.21
## trialNum 148.2 12.18 -0.76
## Residual 1440.3 37.95
## Number of obs: 24176, groups: beeColHive, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 477.420 43.063 11.087
## trthigh 14.953 2.462 6.073
## trtlow 20.415 2.103 9.707
## IT_imputed -37.029 10.446 -3.545
## trialNum 0.787 2.450 0.321
##
## Correlation of Fixed Effects:
## (Intr) trthgh trtlow IT_mpt
## trthigh 0.080
## trtlow -0.017 0.012
## IT_imputed -0.992 -0.084 0.014
## trialNum -0.005 -0.042 -0.070 -0.090m5 <- update(m4, .~. - IT_imputed)
anova(m4, m5) # keep IT, based on BIC## Data: sl
## Models:
## m5: freq ~ trt + trialNum + (1 + trialNum | beeColHive)
## m4: freq ~ trt + IT_imputed + trialNum + (1 + trialNum | beeColHive)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m5 8 244757 244822 -122371 244741
## m4 9 244748 244821 -122365 244730 11.294 1 0.0007777 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m5 <- update(m4, .~. - trt)
anova(m4, m5) # keep trt## Data: sl
## Models:
## m5: freq ~ IT_imputed + trialNum + (1 + trialNum | beeColHive)
## m4: freq ~ trt + IT_imputed + trialNum + (1 + trialNum | beeColHive)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m5 7 244872 244929 -122429 244858
## m4 9 244748 244821 -122365 244730 128 2 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# refit best model with REML = TRUE
m3 <- update(m4, .~., REML = TRUE)
summary(m3) # summary for paper -- leave trial number in, to account for random effect## Linear mixed model fit by REML ['lmerMod']
## Formula: freq ~ trt + IT_imputed + trialNum + (1 + trialNum | beeColHive)
## Data: sl
##
## REML criterion at convergence: 244708.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9969 -0.5117 0.1598 0.6805 4.0009
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beeColHive (Intercept) 1164.2 34.12
## trialNum 160.2 12.66 -0.76
## Residual 1440.3 37.95
## Number of obs: 24176, groups: beeColHive, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 478.0876 44.2109 10.814
## trthigh 14.9576 2.4656 6.066
## trtlow 20.4750 2.1078 9.714
## IT_imputed -37.2000 10.7254 -3.468
## trialNum 0.8128 2.5471 0.319
##
## Correlation of Fixed Effects:
## (Intr) trthgh trtlow IT_mpt
## trthigh 0.078
## trtlow -0.015 0.011
## IT_imputed -0.992 -0.081 0.012
## trialNum -0.007 -0.040 -0.068 -0.089plot(m3)
qqnorm(ranef(m3)$beeColHive[[1]])
qqline(ranef(m3)$beeColHive[[1]])
plot_model(m3) ## Computing p-values via Wald-statistics approximation (treating t as Wald z).
# plot random effects to find any outliers
plot_model(m3, type = "re")
# post-hoc tests -- pvals for paper
summary(glht(m3, linfct = mcp(trt = "Tukey")), test = adjusted("none"))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = freq ~ trt + IT_imputed + trialNum + (1 + trialNum |
## beeColHive), data = sl, REML = TRUE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## high - full == 0 14.958 2.466 6.066 1.31e-09 ***
## low - full == 0 20.475 2.108 9.714 < 2e-16 ***
## low - high == 0 5.517 3.225 1.711 0.0872 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- none method)## bonf adjusted pvals
summary(glht(m3, linfct = mcp(trt = "Tukey")), test = adjusted("bonf"))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = freq ~ trt + IT_imputed + trialNum + (1 + trialNum |
## beeColHive), data = sl, REML = TRUE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## high - full == 0 14.958 2.466 6.066 3.92e-09 ***
## low - full == 0 20.475 2.108 9.714 < 2e-16 ***
## low - high == 0 5.517 3.225 1.711 0.261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- bonferroni method)Generate CI’s
# set number of bootstrap samples
# refref: change to 10K before knitting
# refref time this
nbootSims = 1000 # ~ 80 minutes on Macbook pro for 10K bootstrap samples, 8min for 1k samples
ITmean = mean(tapply(sl$IT_imputed, INDEX = sl$beeCol, FUN = function(x) x[1] ))
print(ITmean)## [1] 4.092619# don't need hive, because that's not in the model we chose (above)
pframe <- data.frame(expand.grid(trt = levels(sl$trt),
IT_imputed = seq(min(s2$IT_imputed), max(s2$IT_imputed), length.out = 50),
beeColHive = 99999,
trialNum = 2))
pframe$freq <- 0
pp <- predict(m3, newdata = pframe, re.form=NA, type = 'response') # re.form sets all random effects to 0
### Calculate CI's (using bootstrap, not accounting for random effects)
system.time({
bb2 <- bootMer(m3, FUN=function(x) predict(x, pframe, re.form=NA, type = 'response'), nsim = nbootSims)
})## user system elapsed
## 499.391 8.727 510.653print(paste("Number of bootstrap samples", nrow(bb2$t)))## [1] "Number of bootstrap samples 1000"bb2_se <-apply(bb2$t,2,function(x) quantile(x, probs = c(0.025, 0.975)))
pframe$blo<-bb2_se[1,]
pframe$bhi<-bb2_se[2,]
pframe$predMean <- pp
pframe <- pframe[, c('trt',"IT_imputed", "blo", "bhi", "predMean")]
pframe## trt IT_imputed blo bhi predMean
## 1 full 3.480000 334.2230 365.8125 350.2571
## 2 high 3.480000 348.2084 381.7324 365.2147
## 3 low 3.480000 353.7201 386.1669 370.7321
## 4 full 3.509796 333.6298 364.1931 349.1487
## 5 high 3.509796 347.6797 379.8411 364.1063
## 6 low 3.509796 353.1270 384.6172 369.6236
## 7 full 3.539592 333.0366 362.7300 348.0403
## 8 high 3.539592 347.0867 378.1713 362.9979
## 9 low 3.539592 352.2732 383.0708 368.5152
## 10 full 3.569388 332.5813 360.9392 346.9319
## 11 high 3.569388 346.6382 376.6180 361.8895
## 12 low 3.569388 351.6522 381.5019 367.4068
## 13 full 3.599184 332.1179 359.1502 345.8234
## 14 high 3.599184 346.2114 375.1408 360.7811
## 15 low 3.599184 351.1807 379.9709 366.2984
## 16 full 3.628980 331.4765 357.4821 344.7150
## 17 high 3.628980 345.7846 373.6272 359.6726
## 18 low 3.628980 350.7446 378.2269 365.1900
## 19 full 3.658776 330.7758 355.9791 343.6066
## 20 high 3.658776 345.3069 371.9689 358.5642
## 21 low 3.658776 350.1533 376.5867 364.0816
## 22 full 3.688571 330.2518 354.3378 342.4982
## 23 high 3.688571 344.6525 370.3169 357.4558
## 24 low 3.688571 349.5669 374.9649 362.9732
## 25 full 3.718367 329.7984 352.7185 341.3898
## 26 high 3.718367 344.1931 368.6549 356.3474
## 27 low 3.718367 348.9885 373.2344 361.8648
## 28 full 3.748163 329.0903 351.0905 340.2814
## 29 high 3.748163 343.4236 367.0058 355.2390
## 30 low 3.748163 348.3807 371.6339 360.7564
## 31 full 3.777959 328.3977 349.6511 339.1730
## 32 high 3.777959 342.8873 365.3602 354.1306
## 33 low 3.777959 347.8572 370.2670 359.6480
## 34 full 3.807755 327.6790 348.2613 338.0646
## 35 high 3.807755 342.2031 363.9237 353.0222
## 36 low 3.807755 347.1802 368.9554 358.5396
## 37 full 3.837551 327.0812 346.8347 336.9562
## 38 high 3.837551 341.6321 362.6070 351.9138
## 39 low 3.837551 346.6085 367.4033 357.4311
## 40 full 3.867347 326.4866 345.5319 335.8478
## 41 high 3.867347 340.9063 361.0545 350.8054
## 42 low 3.867347 345.8188 365.7878 356.3227
## 43 full 3.897143 325.8903 343.9881 334.7394
## 44 high 3.897143 340.3471 359.8770 349.6970
## 45 low 3.897143 344.9912 364.4010 355.2143
## 46 full 3.926939 325.1045 342.6451 333.6309
## 47 high 3.926939 339.3762 358.3934 348.5886
## 48 low 3.926939 344.2982 363.2147 354.1059
## 49 full 3.956735 324.3076 341.4876 332.5225
## 50 high 3.956735 338.6995 357.1928 347.4801
## 51 low 3.956735 343.6205 362.0037 352.9975
## 52 full 3.986531 323.5189 340.0817 331.4141
## 53 high 3.986531 337.9123 355.8932 346.3717
## 54 low 3.986531 342.8694 360.8123 351.8891
## 55 full 4.016327 322.5212 338.7161 330.3057
## 56 high 4.016327 337.0241 354.5992 345.2633
## 57 low 4.016327 342.1285 359.7719 350.7807
## 58 full 4.046122 321.6481 337.3550 329.1973
## 59 high 4.046122 336.1289 353.3168 344.1549
## 60 low 4.046122 341.2816 358.4632 349.6723
## 61 full 4.075918 320.8410 335.9266 328.0889
## 62 high 4.075918 335.0800 351.9709 343.0465
## 63 low 4.075918 340.2646 357.1499 348.5639
## 64 full 4.105714 319.6784 334.8615 326.9805
## 65 high 4.105714 333.8890 351.0246 341.9381
## 66 low 4.105714 339.4489 356.0236 347.4555
## 67 full 4.135510 318.7003 333.6862 325.8721
## 68 high 4.135510 332.6686 349.8800 340.8297
## 69 low 4.135510 338.3215 354.8551 346.3470
## 70 full 4.165306 317.7977 332.4643 324.7637
## 71 high 4.165306 331.5236 348.5609 339.7213
## 72 low 4.165306 337.4235 353.6575 345.2386
## 73 full 4.195102 316.5609 331.2811 323.6553
## 74 high 4.195102 330.6223 347.6174 338.6129
## 75 low 4.195102 336.1701 352.7036 344.1302
## 76 full 4.224898 315.4364 330.6213 322.5468
## 77 high 4.224898 329.4735 346.7067 337.5045
## 78 low 4.224898 335.0352 351.8690 343.0218
## 79 full 4.254694 314.3151 329.8146 321.4384
## 80 high 4.254694 328.1974 345.8902 336.3961
## 81 low 4.254694 333.5943 350.9359 341.9134
## 82 full 4.284490 312.7474 328.8880 320.3300
## 83 high 4.284490 327.0407 345.0082 335.2876
## 84 low 4.284490 332.3179 350.2661 340.8050
## 85 full 4.314286 311.2283 327.9078 319.2216
## 86 high 4.314286 325.8545 344.1932 334.1792
## 87 low 4.314286 330.9554 349.4553 339.6966
## 88 full 4.344082 309.8186 327.1624 318.1132
## 89 high 4.344082 324.3573 343.4462 333.0708
## 90 low 4.344082 329.5286 348.6454 338.5882
## 91 full 4.373878 308.4691 326.2726 317.0048
## 92 high 4.373878 322.8533 342.3399 331.9624
## 93 low 4.373878 328.0837 347.6198 337.4798
## 94 full 4.403673 306.9018 325.4300 315.8964
## 95 high 4.403673 321.4102 341.4471 330.8540
## 96 low 4.403673 326.6759 346.9153 336.3714
## 97 full 4.433469 305.2534 324.7720 314.7880
## 98 high 4.433469 320.1029 340.5593 329.7456
## 99 low 4.433469 325.0767 346.0657 335.2630
## 100 full 4.463265 303.5359 324.1786 313.6796
## 101 high 4.463265 318.6195 339.8592 328.6372
## 102 low 4.463265 323.5343 345.1844 334.1545
## 103 full 4.493061 301.9081 323.6283 312.5712
## 104 high 4.493061 317.2082 339.3105 327.5288
## 105 low 4.493061 322.0799 344.4173 333.0461
## 106 full 4.522857 300.3723 322.9978 311.4628
## 107 high 4.522857 315.7810 338.8188 326.4204
## 108 low 4.522857 320.5397 343.9762 331.9377
## 109 full 4.552653 298.8682 322.4423 310.3543
## 110 high 4.552653 314.2647 338.0426 325.3120
## 111 low 4.552653 319.0327 343.2451 330.8293
## 112 full 4.582449 297.3124 321.8047 309.2459
## 113 high 4.582449 312.7455 337.4507 324.2036
## 114 low 4.582449 317.2149 342.4412 329.7209
## 115 full 4.612245 295.6605 321.1666 308.1375
## 116 high 4.612245 311.0650 336.9411 323.0951
## 117 low 4.612245 315.5953 341.8644 328.6125
## 118 full 4.642041 293.8647 320.5262 307.0291
## 119 high 4.642041 309.3843 336.4406 321.9867
## 120 low 4.642041 314.1394 340.9325 327.5041
## 121 full 4.671837 292.3362 319.8834 305.9207
## 122 high 4.671837 307.8294 335.7673 320.8783
## 123 low 4.671837 312.5157 340.4934 326.3957
## 124 full 4.701633 290.7996 319.1231 304.8123
## 125 high 4.701633 306.1351 335.0848 319.7699
## 126 low 4.701633 311.0474 339.8004 325.2873
## 127 full 4.731429 289.2914 318.6065 303.7039
## 128 high 4.731429 304.5320 334.3269 318.6615
## 129 low 4.731429 309.3297 339.5053 324.1789
## 130 full 4.761224 287.7832 317.9657 302.5955
## 131 high 4.761224 302.8711 333.5504 317.5531
## 132 low 4.761224 307.6110 338.7806 323.0705
## 133 full 4.791020 286.2531 317.4142 301.4871
## 134 high 4.791020 301.0496 333.0360 316.4447
## 135 low 4.791020 305.9091 338.2959 321.9620
## 136 full 4.820816 284.4511 317.0050 300.3787
## 137 high 4.820816 299.3329 332.4588 315.3363
## 138 low 4.820816 304.1734 337.8113 320.8536
## 139 full 4.850612 282.7752 316.5567 299.2703
## 140 high 4.850612 297.6552 331.8396 314.2279
## 141 low 4.850612 302.4548 337.3094 319.7452
## 142 full 4.880408 281.0615 316.0741 298.1618
## 143 high 4.880408 295.9175 331.2325 313.1195
## 144 low 4.880408 300.7385 336.7415 318.6368
## 145 full 4.910204 279.2977 315.4952 297.0534
## 146 high 4.910204 294.0649 330.7872 312.0111
## 147 low 4.910204 299.0276 336.1702 317.5284
## 148 full 4.940000 277.5930 314.9527 295.9450
## 149 high 4.940000 292.2124 330.3399 310.9026
## 150 low 4.940000 297.3826 335.5993 316.4200Make plots for paper
#plot 95% confidence intervals
# "Mean and bootstrap CI based on fixed-effects uncertainty ONLY"
# rename labels for plot
pframe$trt3 = plyr::mapvalues(pframe$trt, from = c("full", "high", "low"),
to = c("Full range\n(220 - 450 Hz)",
"High range\n(340 - 390 Hz)",
"Low range\n(220 - 330 Hz)"))
s44 <- full_join(s2, s4)## Joining, by = c("beeColHive", "trt2", "trt3")s2## # A tibble: 24 x 4
## beeColHive trt2 IT_imputed trt3
## <fct> <chr> <dbl> <chr>
## 1 green.4 full_2 4.21 "Full range\n(220 - 450 Hz)"
## 2 limeblue.5 full_2 4.69 "Full range\n(220 - 450 Hz)"
## 3 limeorange.5 full_2 3.98 "Full range\n(220 - 450 Hz)"
## 4 limepurple.5 low 3.48 "Low range\n(220 - 330 Hz)"
## 5 limered.5 low 4.15 "Low range\n(220 - 330 Hz)"
## 6 limewhite.5 full_2 3.76 "Full range\n(220 - 450 Hz)"
## 7 orange.3 high 3.99 "High range\n(340 - 390 Hz)"
## 8 redblue.4 full_2 3.75 "Full range\n(220 - 450 Hz)"
## 9 redgreen.5 full_2 4.12 "Full range\n(220 - 450 Hz)"
## 10 redpink.5 low 4.21 "Low range\n(220 - 330 Hz)"
## # ... with 14 more rowss44 %>% print(n = Inf)## # A tibble: 24 x 6
## beeColHive trt2 IT_imputed trt3 mean_freq sd_freq
## <fct> <chr> <dbl> <chr> <dbl> <dbl>
## 1 green.4 full_2 4.21 "Full range\n(220 - … 285 59.2
## 2 limeblue.5 full_2 4.69 "Full range\n(220 - … 304. 40.1
## 3 limeorange.5 full_2 3.98 "Full range\n(220 - … 345. 26.7
## 4 limepurple.5 low 3.48 "Low range\n(220 - 3… 344. 32.6
## 5 limered.5 low 4.15 "Low range\n(220 - 3… 365. 37.1
## 6 limewhite.5 full_2 3.76 "Full range\n(220 - … 327. 35.5
## 7 orange.3 high 3.99 "High range\n(340 - … 382. 27.5
## 8 redblue.4 full_2 3.75 "Full range\n(220 - … 341. 43.3
## 9 redgreen.5 full_2 4.12 "Full range\n(220 - … 326. 36.4
## 10 redpink.5 low 4.21 "Low range\n(220 - 3… 290. 39.1
## 11 redpurple.5 high 4.34 "High range\n(340 - … 336. 36.2
## 12 white.4 full_2 4.03 "Full range\n(220 - … 314. 45.7
## 13 whiteblue.5 low 4.34 "Low range\n(220 - 3… 348. 33.4
## 14 whitegold.5 low 4.63 "Low range\n(220 - 3… 330. 40.8
## 15 whiteorange.5 low 4.46 "Low range\n(220 - 3… 361. 39.1
## 16 whitepink.5 high 4.59 "High range\n(340 - … 326. 29.9
## 17 whitered.5 high 4.42 "High range\n(340 - … 322. 43.4
## 18 whiteyellow.5 full_2 4.24 "Full range\n(220 - … 324. 32.9
## 19 yellowblue.5 high 4.94 "High range\n(340 - … 309. 44.0
## 20 yellowgreen.5 full_2 4.14 "Full range\n(220 - … 283. 35.5
## 21 yelloworange… high 4.34 "High range\n(340 - … 312. 35.9
## 22 yellowpink.5 low 3.87 "Low range\n(220 - 3… 346. 40.8
## 23 yellowpurple… low 3.82 "Low range\n(220 - 3… 361. 41.8
## 24 yellowred.5 full_2 4.07 "Full range\n(220 - … 339. 32.3set.seed(1234)
g0 <- ggplot(pframe, aes(x=IT_imputed, y=predMean, color = trt3, fill = trt3))+
geom_line()+
facet_wrap(~trt3) +
geom_rug(data = s44, aes(x = IT_imputed, color = trt3, y = s44$mean_freq),
inherit.aes = FALSE, sides = "b",
position = position_jitter(height = 0, width = 0.03)) +
labs(y = "Sonication frequency (Hz)", x = "Intertegular span (mm)") +
geom_ribbon(aes(ymin = blo, ymax = bhi), alpha = 0.3, color = NA)+
# geom_point(data = s44, aes(y= mean_freq)) +
# geom_errorbar(data = s44, aes(x = IT_imputed, ymin = mean_freq - sd_freq,
# ymax = mean_freq + sd_freq, color = trt3),
# inherit.aes = FALSE, width = 0.1) +
theme(plot.background = element_rect(fill = "transparent",colour = NA),
panel.background = element_rect(fill = "transparent",colour = NA),
legend.position ="none",
legend.background = element_rect(fill="transparent", colour = NA),
strip.background = element_blank(),
panel.spacing.x = unit(1, "lines"),
plot.margin = margin(c(0.2,0.51, 0.2, 0.2), unit = "cm")) +
#scale_x_continuous(breaks = seq(3.4, 4.8, by = 0.5)) +
scale_color_viridis_d(name = "Frequency range\nfor reward", end = 0.7, option = "inferno") +
scale_fill_viridis_d(name = "Frequency range\nfor reward", end =0.7, option = 'inferno')
g0
ggsave(plot = g0, filename = file.path(figDir, "SonicationFreqPredsAndCI_unadjusted.png"), unit = "in", dpi = 500,
width = 5, height = 4)
ggsave(plot = g0, filename = file.path(figDir, "SonicationFreqPredsAndCI_unadjusted.svg"), width = 5, height = 4)make a different version of the plot
g1 <- ggplot(pframe, aes(x=IT_imputed, y=predMean, color = trt3, fill = trt3))+
geom_line()+
#facet_wrap(~trt3) +
# geom_rug(data = s44, aes(x = IT_imputed, color = trt3, y = s44$mean_freq),
# inherit.aes = FALSE, sides = "b",
# position = position_jitter(height = 0, width = 0.03)) +
labs(y = "Sonication frequency (Hz)", x = "Intertegular span (mm)") +
geom_ribbon(aes(ymin = blo, ymax = bhi), alpha = 0.3, color = NA)+
# geom_point(data = s44, aes(y= mean_freq)) +
# geom_errorbar(data = s44, aes(x = IT_imputed, ymin = mean_freq - sd_freq,
# ymax = mean_freq + sd_freq, color = trt3),
# inherit.aes = FALSE, width = 0.1) +
theme(plot.background = element_rect(fill = "transparent",colour = NA),
panel.background = element_rect(fill = "transparent",colour = NA),
legend.position =c(0.8, 0.8),
legend.background = element_rect(fill="transparent", colour = NA),
strip.background = element_blank(),
legend.title = element_text(size = 8),
panel.spacing.x = unit(1, "lines"),
plot.margin = margin(c(0.2,0.51, 0.2, 0.2), unit = "cm")) +
#scale_x_continuous(breaks = seq(3.4, 4.8, by = 0.5)) +
scale_color_viridis_d(name = "Frequency range\nfor reward", end = 0.7, option = "inferno") +
scale_fill_viridis_d(name = "Frequency range\nfor reward", end =0.7, option = 'inferno')
g1
ggsave(plot = g1, filename = file.path(figDir, "SonicationFreqPredsAndCI_unadjusted_overlap.png"), unit = "in", dpi = 500,
width = 6.5/2, height = 2.5)
svg(file.path(figDir, "SonicationFreqPredsAndCI_unadjusted_overlap.svg"), width = 6.5/2, height = 4)
g1
dev.off()## quartz_off_screen
## 2visualization of frequency over time
sl <- read_csv(file.path(dataDir, '01_CombinedTrials_cleaned.csv'))## Parsed with column specification:
## cols(
## .default = col_integer(),
## beeCol = col_character(),
## meanFreq = col_double(),
## IT_imputed = col_double(),
## freq = col_double(),
## amp = col_double(),
## datetime = col_character(),
## rewTF = col_logical(),
## BeeNumCol = col_character(),
## accFile = col_character(),
## datetime_str = col_datetime(format = ""),
## IT = col_double(),
## trt = col_character(),
## amp_acc = col_double()
## )## See spec(...) for full column specifications.colnames(sl)## [1] "beeCol" "hive" "meanFreq" "IT_imputed"
## [5] "index" "freq" "amp" "datetime"
## [9] "rewNum" "rewTF" "lowRewAmp" "highrewAmp"
## [13] "BeeNumCol" "accFile" "trialNum" "datetime_str"
## [17] "lowFrq" "highFrq" "IT" "trt"
## [21] "amp_acc"sl <- sl %>%
# gets rid of the copious warnings
mutate(timeSinceStart = NA, timeSinceLastBuzz = NA, buzzesSinceReward = NA) %>%
mutate(beeColHive = interaction(beeCol, hive)) %>%
# remove two trials I messed up
filter(!(beeCol == "whitepink" & trialNum == 3)) %>%
filter(!(beeCol == "limepurple" & trialNum == 3)) %>%
mutate(t5 = recode(.$trt, "full" = "Full range\n(220 - 450 Hz)",
"high" = "High range\n(340 - 390 Hz)\nsecond trial",
"low" = "Low range\n(220 - 330 Hz)\nsecond trial")) %>%
mutate(t6 = t5,
t6 = ifelse(.$trt == 'full' & .$trialNum == 1,
"Full range \n(220 - 450 Hz)\nfirst trial", t5),
t6 = ifelse(.$trt == 'full' & .$trialNum > 1,
"Full range \n(220 - 450 Hz)\nsecond trial", t6)
)
st <- sl %>%
filter(trialNum <= 10 & trialNum > 1)
fta <- sl %>%
filter(trialNum == 1) %>%
dplyr::group_by(beeColHive) %>%
dplyr::summarise(initialFreq = mean(freq)) %>%
full_join(sl) %>%
mutate(freqDiff = freq- initialFreq) %>%
filter(beeColHive %in% .$beeColHive[.$trialNum > 1]) %>%
dplyr::arrange(trt) ## Joining, by = "beeColHive"fta[fta$beeColHive == "limepurple.5", ]## # A tibble: 296 x 29
## beeColHive initialFreq beeCol hive meanFreq IT_imputed index freq
## <fct> <dbl> <chr> <int> <dbl> <dbl> <int> <dbl>
## 1 limepurpl… 352. limep… 5 341. 3.48 1 410
## 2 limepurpl… 352. limep… 5 341. 3.48 2 310
## 3 limepurpl… 352. limep… 5 341. 3.48 3 390
## 4 limepurpl… 352. limep… 5 341. 3.48 4 370
## 5 limepurpl… 352. limep… 5 341. 3.48 5 370
## 6 limepurpl… 352. limep… 5 341. 3.48 6 330
## 7 limepurpl… 352. limep… 5 341. 3.48 7 360
## 8 limepurpl… 352. limep… 5 341. 3.48 8 360
## 9 limepurpl… 352. limep… 5 341. 3.48 9 240
## 10 limepurpl… 352. limep… 5 341. 3.48 10 300
## # ... with 286 more rows, and 21 more variables: amp <dbl>,
## # datetime <chr>, rewNum <int>, rewTF <lgl>, lowRewAmp <int>,
## # highrewAmp <int>, BeeNumCol <chr>, accFile <chr>, trialNum <int>,
## # datetime_str <dttm>, lowFrq <int>, highFrq <int>, IT <dbl>, trt <chr>,
## # amp_acc <dbl>, timeSinceStart <lgl>, timeSinceLastBuzz <lgl>,
## # buzzesSinceReward <lgl>, t5 <chr>, t6 <chr>, freqDiff <dbl>cc <- fta %>%
filter(trialNum > 1) %>%
group_by(beeColHive) %>%
summarize(meanFreq = mean(freq))
cc %>% print(n = Inf)## # A tibble: 24 x 2
## beeColHive meanFreq
## <fct> <dbl>
## 1 orange.3 382.
## 2 green.4 285
## 3 redblue.4 341.
## 4 white.4 314.
## 5 limeblue.5 304.
## 6 limeorange.5 345.
## 7 limepurple.5 344.
## 8 limered.5 365.
## 9 limewhite.5 327.
## 10 redgreen.5 326.
## 11 redpink.5 290.
## 12 redpurple.5 336.
## 13 whiteblue.5 348.
## 14 whitegold.5 330.
## 15 whiteorange.5 361.
## 16 whitepink.5 326.
## 17 whitered.5 322.
## 18 whiteyellow.5 324.
## 19 yellowblue.5 309.
## 20 yellowgreen.5 283.
## 21 yelloworange.5 312.
## 22 yellowpink.5 346.
## 23 yellowpurple.5 361.
## 24 yellowred.5 339.bb <- fta %>%
dplyr::select(beeColHive, trt, trialNum, initialFreq) %>%
dplyr::filter(trialNum == 2 | trialNum == 3) %>%
group_by(beeColHive) %>%
slice(1:1) %>%
full_join(cc) %>%
mutate(freqDiff = initialFreq - meanFreq) %>%
dplyr::arrange(trt, desc(freqDiff))## Joining, by = "beeColHive"bb## # A tibble: 24 x 6
## # Groups: beeColHive [24]
## beeColHive trt trialNum initialFreq meanFreq freqDiff
## <fct> <chr> <int> <dbl> <dbl> <dbl>
## 1 green.4 full 2 319. 285 33.9
## 2 white.4 full 3 343. 314. 28.8
## 3 yellowgreen.5 full 2 298. 283. 14.7
## 4 redgreen.5 full 2 335. 326. 8.63
## 5 redblue.4 full 3 342. 341. 1.03
## 6 limeblue.5 full 3 302. 304. -1.93
## 7 yellowred.5 full 2 320. 339. -19.3
## 8 whiteyellow.5 full 2 303. 324. -20.6
## 9 limewhite.5 full 2 292 327. -35.1
## 10 limeorange.5 full 2 286. 345. -58.8
## # ... with 14 more rowsfta <- fta %>%
mutate(beeColHive = factor(beeColHive, levels = bb$beeColHive)) %>%
mutate(t5 = recode(.$trt, "full" = "Full range\n(220 - 450 Hz)",
"high" = "High range\n(340 - 390 Hz)",
"low" = "Low range\n(220 - 330 Hz)")) %>%
filter(trialNum <= 10)
s44 = fta %>%
group_by(trialNum, beeColHive, t5) %>%
dplyr::summarise(meanFreq2 = mean(freq))
s44## # A tibble: 175 x 4
## # Groups: trialNum, beeColHive [?]
## trialNum beeColHive t5 meanFreq2
## <int> <fct> <chr> <dbl>
## 1 1 green.4 "Full range\n(220 - 450 Hz)" 319.
## 2 1 white.4 "Full range\n(220 - 450 Hz)" 343.
## 3 1 yellowgreen.5 "Full range\n(220 - 450 Hz)" 298.
## 4 1 redgreen.5 "Full range\n(220 - 450 Hz)" 335.
## 5 1 redblue.4 "Full range\n(220 - 450 Hz)" 342.
## 6 1 limeblue.5 "Full range\n(220 - 450 Hz)" 302.
## 7 1 yellowred.5 "Full range\n(220 - 450 Hz)" 320.
## 8 1 whiteyellow.5 "Full range\n(220 - 450 Hz)" 303.
## 9 1 limewhite.5 "Full range\n(220 - 450 Hz)" 292
## 10 1 limeorange.5 "Full range\n(220 - 450 Hz)" 286.
## # ... with 165 more rows# plot to show trials
ggplot(fta, aes(x = trialNum, y = freq, color = t5)) +
geom_ribbon(data = fta[fta$trialNum > 1,],
aes(x = rep(seq(1.5, 12, 3),
length.out = nrow(fta[fta$trialNum > 1,])),
ymin = lowFrq, ymax = highFrq), alpha = 0.2, color = NA) +
geom_point(position = position_jitter(width = 0, height = 10),
alpha= 0.2, stroke = 0, aes( shape = t5 )) +
facet_wrap(~beeColHive, ncol = 8) +
geom_line(data = s44, aes(x = trialNum, y = meanFreq2), color = 'black', lwd = 0.8, show.legend = FALSE) +
geom_line(data = s44, aes(x = trialNum, y = meanFreq2, color = t5), lwd = 0.8, show.legend = FALSE) +
geom_point(data = s44, aes(x = trialNum, y = meanFreq2),
color = 'black', pch = 18, show.legend = FALSE) +
geom_ribbon(data = fta[fta$trialNum > 1,],
aes(x = rep(seq(0.5, 1.5, 1),
length.out = nrow(fta[fta$trialNum > 1,])),
ymin = 220, ymax = 450), alpha = 0.2, color = NA) +
geom_hline(aes(yintercept = initialFreq)) +
# stat_smooth(data = fta[fta$trialNum > 1,],
# method = 'loess', se = FALSE,
# size =1.5, color = 'black', span = 0.6) +
# stat_smooth(data = fta[fta$trialNum > 1,],
# method = 'loess',
# se = FALSE, size =1, span = 0.6) +
scale_x_continuous(breaks = seq(0, 10, 2)) +
scale_color_viridis_d(name = "",
option = "magma", begin = 0.3,
end = 0.80,
guide = guide_legend(override.aes = list(alpha = 1, size = 4))) +
theme_classic() +
theme(strip.background = element_blank(),
strip.text = element_blank(),
panel.border = element_rect(colour = "grey40",
fill=NA, size=0.5),
legend.position = 'top') +
scale_shape_manual(name = "", values = c(16,17,15)) +
labs(y = "Frequency (Hz)", x = "Trial Number")## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
ggsave(filename = file.path(figDir,"freqLearningCurve.png"),
dpi = 500, width = 6.5*1.5, height = 4*1.5, units = "in")## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?options(digits.secs=6)
# plot buzzes for first trial
ftrl <- sl %>%
filter(trialNum <= 2 ) %>%
mutate(dateTime_fmt= as.POSIXct(strptime(x = .$datetime_str,
format = "%Y-%m-%d %H:%M:%OS" ))) %>%
arrange(dateTime_fmt) %>%
group_by(beeColHive) %>%
mutate(index = row_number())
dd = sl %>%
filter(trialNum == 2 | trialNum == 3) %>%
mutate(trialNum = 99) %>%
ungroup() %>%
group_by(beeColHive, trt, trialNum) %>%
summarise(meanTT = n()) %>%
arrange(trt, meanTT)
dd## # A tibble: 24 x 4
## # Groups: beeColHive, trt [24]
## beeColHive trt trialNum meanTT
## <fct> <chr> <dbl> <int>
## 1 green.4 full 99 4
## 2 limeorange.5 full 99 51
## 3 limewhite.5 full 99 51
## 4 whiteyellow.5 full 99 102
## 5 limeblue.5 full 99 104
## 6 redgreen.5 full 99 104
## 7 yellowgreen.5 full 99 106
## 8 white.4 full 99 113
## 9 yellowred.5 full 99 114
## 10 redblue.4 full 99 127
## # ... with 14 more rowsdd %>% print(n = Inf)## # A tibble: 24 x 4
## # Groups: beeColHive, trt [24]
## beeColHive trt trialNum meanTT
## <fct> <chr> <dbl> <int>
## 1 green.4 full 99 4
## 2 limeorange.5 full 99 51
## 3 limewhite.5 full 99 51
## 4 whiteyellow.5 full 99 102
## 5 limeblue.5 full 99 104
## 6 redgreen.5 full 99 104
## 7 yellowgreen.5 full 99 106
## 8 white.4 full 99 113
## 9 yellowred.5 full 99 114
## 10 redblue.4 full 99 127
## 11 orange.3 high 99 11
## 12 whitepink.5 high 99 143
## 13 redpurple.5 high 99 163
## 14 yellowblue.5 high 99 220
## 15 whitered.5 high 99 364
## 16 yelloworange.5 high 99 381
## 17 redpink.5 low 99 129
## 18 whitegold.5 low 99 142
## 19 limepurple.5 low 99 243
## 20 yellowpink.5 low 99 245
## 21 whiteblue.5 low 99 621
## 22 limered.5 low 99 632
## 23 whiteorange.5 low 99 692
## 24 yellowpurple.5 low 99 706ee = sl %>%
filter(trialNum == 1) %>%
ungroup() %>%
group_by(beeColHive, trt, trialNum) %>%
summarise(meanTT = n()) %>%
rbind(dd) %>%
arrange(beeColHive, desc(trialNum)) %>%
group_by(beeColHive) %>%
filter(row_number(beeColHive) == 1) %>%
arrange(trt, meanTT)
ee %>% print(n = Inf)## # A tibble: 43 x 4
## # Groups: beeColHive [43]
## beeColHive trt trialNum meanTT
## <fct> <chr> <dbl> <int>
## 1 goldred.4 full 1 3
## 2 limesilver.5 full 1 3
## 3 green.4 full 99 4
## 4 purple.3 full 1 9
## 5 silver.5 full 1 26
## 6 lime.5 full 1 28
## 7 blue.4 full 1 36
## 8 whitegreen.4 full 1 39
## 9 limegreen.5 full 1 50
## 10 orangeblue.5 full 1 50
## 11 orangegreen.5 full 1 50
## 12 orangepink.5 full 1 50
## 13 orangepurple.5 full 1 50
## 14 limeorange.5 full 99 51
## 15 limepink.5 full 1 51
## 16 limewhite.5 full 99 51
## 17 orange.5 full 1 51
## 18 limeyellow.5 full 1 52
## 19 gold.3 full 1 54
## 20 limegold.5 full 1 54
## 21 whitepurple.5 full 1 55
## 22 limepurpleyellow.5 full 1 58
## 23 whiteyellow.5 full 99 102
## 24 limeblue.5 full 99 104
## 25 redgreen.5 full 99 104
## 26 yellowgreen.5 full 99 106
## 27 white.4 full 99 113
## 28 yellowred.5 full 99 114
## 29 redblue.4 full 99 127
## 30 orange.3 high 99 11
## 31 whitepink.5 high 99 143
## 32 redpurple.5 high 99 163
## 33 yellowblue.5 high 99 220
## 34 whitered.5 high 99 364
## 35 yelloworange.5 high 99 381
## 36 redpink.5 low 99 129
## 37 whitegold.5 low 99 142
## 38 limepurple.5 low 99 243
## 39 yellowpink.5 low 99 245
## 40 whiteblue.5 low 99 621
## 41 limered.5 low 99 632
## 42 whiteorange.5 low 99 692
## 43 yellowpurple.5 low 99 706unique(dd$beeColHive)## [1] green.4 limeorange.5 limewhite.5 whiteyellow.5
## [5] limeblue.5 redgreen.5 yellowgreen.5 white.4
## [9] yellowred.5 redblue.4 orange.3 whitepink.5
## [13] redpurple.5 yellowblue.5 whitered.5 yelloworange.5
## [17] redpink.5 whitegold.5 limepurple.5 yellowpink.5
## [21] whiteblue.5 limered.5 whiteorange.5 yellowpurple.5
## 126 Levels: blue.3 gold.3 goldred.3 green.3 lime.3 ... yellowred.5unique(ftrl$beeColHive)## [1] blue.4 orange.3 white.4
## [4] gold.3 redblue.4 green.4
## [7] purple.3 whitegreen.4 whitepink.5
## [10] whitepurple.5 goldred.4 whiteorange.5
## [13] whiteyellow.5 whitegold.5 whiteblue.5
## [16] whitered.5 yellowred.5 yellowgreen.5
## [19] yellowpurple.5 yelloworange.5 yellowpink.5
## [22] yellowblue.5 redgreen.5 redpink.5
## [25] redpurple.5 silver.5 lime.5
## [28] limered.5 limeblue.5 limesilver.5
## [31] limepurple.5 limewhite.5 limepurpleyellow.5
## [34] limeorange.5 orange.5 limepink.5
## [37] limegreen.5 limeyellow.5 limegold.5
## [40] orangepink.5 orangegreen.5 orangepurple.5
## [43] orangeblue.5
## 126 Levels: blue.3 gold.3 goldred.3 green.3 lime.3 ... yellowred.5ftrl = ftrl %>%
ungroup() %>%
mutate(beeColHive = factor(.$beeColHive, levels = ee$beeColHive)) %>%
filter(beeColHive %in% ee$beeColHive[ee$meanTT > 20])
ggplot(ftrl, aes(x = index, y = freq, color = t6)) +
geom_point(alpha = 0.4, stroke = 0, aes(shape = t6)) +
facet_wrap(~beeColHive, ncol = 8 ) +
stat_smooth(aes(group = t6), method = 'loess', color = "black", se = FALSE, span = 0.7, lwd = 0.4) +
scale_color_viridis_d(name = "",
option = "magma", begin = 0.3,
end = 0.80,
guide = guide_legend(override.aes = list(alpha = 1, size = 4))) +
scale_shape_manual(name = "", values = c(16,17,15, 18)) +
theme_classic() +
theme(strip.background = element_blank(),
strip.text = element_blank(),
panel.border = element_rect(colour = "grey40",
fill=NA, size=0.5),
legend.position = 'top') +
scale_x_continuous(breaks = seq(0, 400, 200), limits = c(0, 400)) +
labs(x = "Sonication Number", y = "Frequency (Hz)") +
geom_ribbon(aes(ymin = lowFrq, ymax = highFrq), alpha = 0.2, color = NA)
ggsave(filename = file.path(figDir,"FreqChangeFirstAndSecTrial.png"),
dpi = 500, width = 6.5*1.5, height = 4*1.5, units = "in")
ggplot(ftrl[ftrl$trialNum == 1,], aes(x = index, y = freq, color = t6)) +
geom_point(alpha = 0.4, stroke = 0, aes(shape = t6)) +
facet_wrap(~beeColHive, ncol = 8 ) +
stat_smooth(aes(group = t6), method = 'loess', color = "black", se = FALSE,span =0.7, lwd = 0.4) +
scale_color_viridis_d(name = "",
option = "magma", begin = 0.3,
end = 0.80,
guide = guide_legend(override.aes = list(alpha = 1, size = 4))) +
scale_shape_manual(name = "", values = c(16,17,15, 18)) +
theme_classic() +
theme(strip.background = element_blank(),
strip.text = element_blank(),
panel.border = element_rect(colour = "grey40",
fill=NA, size=0.5),
legend.position = 'top') +
scale_x_continuous(breaks = seq(0, 50, 20), limits = c(0, 50)) +
labs(x = "Sonication Number", y = "Frequency (Hz)") ## Warning: Removed 106 rows containing non-finite values (stat_smooth).## Warning: Removed 106 rows containing missing values (geom_point).
ggsave(filename = file.path(figDir,"FreqChangeFirstTrial.png"),
dpi = 500, width = 6.5*1.5, height = 4*1.5, units = "in")## Warning: Removed 106 rows containing non-finite values (stat_smooth).
## Warning: Removed 106 rows containing missing values (geom_point).look at acceleration change over time
sl <- sl %>%
# gets rid of the copious warnings
mutate(timeSinceStart = NA, timeSinceLastBuzz = NA, buzzesSinceReward = NA) %>%
mutate(beeColHive = interaction(beeCol, hive)) %>%
# remove two trials I messed up
filter(!(beeCol == "whitepink" & trialNum == 3)) %>%
filter(!(beeCol == "limepurple" & trialNum == 3)) %>%
mutate(amp_acc2 = amp_acc / 2)
fta <- sl %>%
filter(trialNum == 1) %>%
dplyr::group_by(beeColHive) %>%
dplyr::summarise(initialAmp = mean(amp_acc2)) %>%
full_join(sl) %>%
mutate(ampDiff = amp_acc2- initialAmp) %>%
filter(beeColHive %in% .$beeColHive[.$trialNum > 1]) %>%
dplyr::arrange(trt) ## Joining, by = "beeColHive"fta[fta$beeColHive == "limepurple.5", ]## # A tibble: 296 x 30
## beeColHive initialAmp beeCol hive meanFreq IT_imputed index freq amp
## <fct> <dbl> <chr> <int> <dbl> <dbl> <int> <dbl> <dbl>
## 1 limepurpl… 22.0 limep… 5 341. 3.48 1 410 0.766
## 2 limepurpl… 22.0 limep… 5 341. 3.48 2 310 0.142
## 3 limepurpl… 22.0 limep… 5 341. 3.48 3 390 0.482
## 4 limepurpl… 22.0 limep… 5 341. 3.48 4 370 0.439
## 5 limepurpl… 22.0 limep… 5 341. 3.48 5 370 0.482
## 6 limepurpl… 22.0 limep… 5 341. 3.48 6 330 0.368
## 7 limepurpl… 22.0 limep… 5 341. 3.48 7 360 0.572
## 8 limepurpl… 22.0 limep… 5 341. 3.48 8 360 0.538
## 9 limepurpl… 22.0 limep… 5 341. 3.48 9 240 0.684
## 10 limepurpl… 22.0 limep… 5 341. 3.48 10 300 0.413
## # ... with 286 more rows, and 21 more variables: datetime <chr>,
## # rewNum <int>, rewTF <lgl>, lowRewAmp <int>, highrewAmp <int>,
## # BeeNumCol <chr>, accFile <chr>, trialNum <int>, datetime_str <dttm>,
## # lowFrq <int>, highFrq <int>, IT <dbl>, trt <chr>, amp_acc <dbl>,
## # timeSinceStart <lgl>, timeSinceLastBuzz <lgl>,
## # buzzesSinceReward <lgl>, t5 <chr>, t6 <chr>, amp_acc2 <dbl>,
## # ampDiff <dbl>cc <- fta %>%
filter(trialNum > 1) %>%
group_by(beeColHive) %>%
summarize(meanAmp = mean(amp_acc2))
cc %>% print(n = Inf)## # A tibble: 24 x 2
## beeColHive meanAmp
## <fct> <dbl>
## 1 orange.3 28.9
## 2 green.4 9.60
## 3 redblue.4 23.0
## 4 white.4 23.9
## 5 limeblue.5 32.9
## 6 limeorange.5 25.4
## 7 limepurple.5 20.0
## 8 limered.5 19.2
## 9 limewhite.5 20.5
## 10 redgreen.5 40.0
## 11 redpink.5 35.8
## 12 redpurple.5 26.4
## 13 whiteblue.5 42.4
## 14 whitegold.5 31.9
## 15 whiteorange.5 25.9
## 16 whitepink.5 29.0
## 17 whitered.5 38.4
## 18 whiteyellow.5 30.7
## 19 yellowblue.5 33.7
## 20 yellowgreen.5 40.0
## 21 yelloworange.5 35.2
## 22 yellowpink.5 30.0
## 23 yellowpurple.5 31.3
## 24 yellowred.5 28.1# use bb from above, to maintain order
# bb <- fta %>%
# dplyr::select(beeColHive, trt, trialNum, initialAmp) %>%
# dplyr::filter(trialNum == 2 | trialNum == 3) %>%
# group_by(beeColHive) %>%
# slice(1:1) %>%
# full_join(cc) %>%
# mutate(ampDiff = initialAmp - meanAmp) %>%
# dplyr::arrange(trt, desc(ampDiff))
#
# bb
fta <- fta %>%
mutate(beeColHive = factor(beeColHive, levels = bb$beeColHive),
t5 = plyr::mapvalues(.$trt, from = c("full", "high", "low"),
to = c("Full range\n(220 - 450 Hz)",
"High range\n(340 - 390 Hz)", "Low range\n(220 - 330 Hz)"))) %>%
filter(trialNum <= 10)
s44 = fta %>%
group_by(trialNum, beeColHive, t5) %>%
dplyr::summarise(meanamp2 = mean(amp_acc2))
s44## # A tibble: 175 x 4
## # Groups: trialNum, beeColHive [?]
## trialNum beeColHive t5 meanamp2
## <int> <fct> <chr> <dbl>
## 1 1 green.4 "Full range\n(220 - 450 Hz)" 22.1
## 2 1 white.4 "Full range\n(220 - 450 Hz)" 23.9
## 3 1 yellowgreen.5 "Full range\n(220 - 450 Hz)" 30.8
## 4 1 redgreen.5 "Full range\n(220 - 450 Hz)" 39.4
## 5 1 redblue.4 "Full range\n(220 - 450 Hz)" 14.4
## 6 1 limeblue.5 "Full range\n(220 - 450 Hz)" 24.6
## 7 1 yellowred.5 "Full range\n(220 - 450 Hz)" 31.4
## 8 1 whiteyellow.5 "Full range\n(220 - 450 Hz)" 32.8
## 9 1 limewhite.5 "Full range\n(220 - 450 Hz)" 18.8
## 10 1 limeorange.5 "Full range\n(220 - 450 Hz)" 21.6
## # ... with 165 more rows# plot to show trials
ggplot(fta, aes(x = trialNum, y = amp_acc2, color = t5)) +
geom_point(position = position_jitter(width = 0, height = 0 ),
alpha= 0.2, stroke = 0, aes( shape = t5 )) +
facet_wrap(~beeColHive, ncol = 8) +
geom_line(data = s44, aes(x = trialNum, y = meanamp2), color = 'black', lwd = 0.8, show.legend = FALSE) +
geom_line(data = s44, aes(x = trialNum, y = meanamp2, color = t5), lwd = 0.8, show.legend = FALSE) +
geom_point(data = s44, aes(x = trialNum, y = meanamp2),
color = 'black', pch = 18, show.legend = FALSE) +
geom_hline(aes(yintercept = initialAmp)) +
# stat_smooth(data = fta[fta$trialNum > 1,],
# method = 'loess', se = FALSE,
# size =1.5, color = 'black', span = 0.6) +
# stat_smooth(data = fta[fta$trialNum > 1,],
# method = 'loess',
# se = FALSE, size =1, span = 0.6) +
scale_x_continuous(breaks = seq(0, 10, 2)) +
scale_color_viridis_d(name = "",
option = "magma", begin = 0.3,
end = 0.80,
guide = guide_legend(override.aes = list(alpha = 1, size = 4))) +
theme_classic() +
theme(strip.background = element_blank(),
strip.text = element_blank(),
panel.border = element_rect(colour = "grey40",
fill=NA, size=0.5),
legend.position = 'top') +
scale_shape_manual(name = "", values = c(16,17,15)) +
labs( y = expression ("Sonication acceleration "(m~s^{-2})), x = "Trial Number")## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?
ggsave(filename = file.path(figDir,"AmplChangeOverTime.png"),
dpi = 200, width = 6.5*1.5, height = 4*1.5, units = "in")## geom_path: Each group consists of only one observation. Do you need to
## adjust the group aesthetic?____ END OF ANALYSIS FOR PAPER ____
Analysis for acceleration
sl <- read_csv(file.path(dataDir, '01_CombinedTrials_cleaned.csv'))## Parsed with column specification:
## cols(
## .default = col_integer(),
## beeCol = col_character(),
## meanFreq = col_double(),
## IT_imputed = col_double(),
## freq = col_double(),
## amp = col_double(),
## datetime = col_character(),
## rewTF = col_logical(),
## BeeNumCol = col_character(),
## accFile = col_character(),
## datetime_str = col_datetime(format = ""),
## IT = col_double(),
## trt = col_character(),
## amp_acc = col_double()
## )## See spec(...) for full column specifications.colnames(sl)## [1] "beeCol" "hive" "meanFreq" "IT_imputed"
## [5] "index" "freq" "amp" "datetime"
## [9] "rewNum" "rewTF" "lowRewAmp" "highrewAmp"
## [13] "BeeNumCol" "accFile" "trialNum" "datetime_str"
## [17] "lowFrq" "highFrq" "IT" "trt"
## [21] "amp_acc"sl <- sl %>%
# gets rid of the copious warnings
mutate(timeSinceStart = NA, timeSinceLastBuzz = NA, buzzesSinceReward = NA) %>%
# make sure all bee colors are lowercase
mutate(beeCol = tolower(beeCol)) %>%
# fix orange.5, which should be orange.3
mutate(hive = ifelse(hive == "5" & beeCol == "orange", "3", hive)) %>%
# color + hive is an ID var
mutate(beeColHive = interaction(beeCol, hive),
colNum = paste(beeCol, hive, sep = "_")) %>%
# remove two trials I messed up
filter(!(beeCol == "whitepink" & trialNum == 3)) %>%
filter(!(beeCol == "limepurple" & trialNum == 3)) %>%
# convert hive to factor
mutate(hive = as.factor(hive),
trt2 = ifelse(trt == 'full' & trialNum > 1,
"full_2", as.character(trt)),
trt = relevel(factor(trt), ref = "full")) %>%
# divide amplitud by 2
mutate(amp_acc2 = amp_acc / 2) %>%
mutate(trialNum0 = trialNum - 1)
# interaction model
nrow(sl)## [1] 24176# note log-transformation to make model fit assumptions better
maa0 = lmer(log(amp_acc2) ~ trt* IT_imputed + hive + trialNum + (1+trialNum|beeColHive), data = sl, REML = FALSE)
# main effect model
maa1 = lmer(log(amp_acc2) ~ trt+ IT_imputed + hive + trialNum +(1+trialNum|beeColHive), data = sl, REML = FALSE)
BIC(maa0, maa1) # use no interaction (keep maa1)## df BIC
## maa0 13 44127.96
## maa1 11 44119.14anova(maa0, maa1) # note that this disagrees with likelihood ratio test## Data: sl
## Models:
## maa1: log(amp_acc2) ~ trt + IT_imputed + hive + trialNum + (1 + trialNum |
## maa1: beeColHive)
## maa0: log(amp_acc2) ~ trt * IT_imputed + hive + trialNum + (1 + trialNum |
## maa0: beeColHive)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## maa1 11 44030 44119 -22004 44008
## maa0 13 44023 44128 -21998 43997 11.367 2 0.003402 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1maa2 = update(maa1, .~. - trt)
BIC(maa1, maa2) # keep treatment (maa1)## df BIC
## maa1 11 44119.14
## maa2 9 44124.30anova(maa1, maa2) # agrees with LRT; p-val for paper, if needed## Data: sl
## Models:
## maa2: log(amp_acc2) ~ IT_imputed + hive + trialNum + (1 + trialNum |
## maa2: beeColHive)
## maa1: log(amp_acc2) ~ trt + IT_imputed + hive + trialNum + (1 + trialNum |
## maa1: beeColHive)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## maa2 9 44051 44124 -22017 44033
## maa1 11 44030 44119 -22004 44008 25.342 2 3.141e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1maa3 <- update(maa1, .~. - hive)
BIC(maa1, maa3) # remove hive (maa3)## df BIC
## maa1 11 44119.14
## maa3 9 44104.53anova(maa1, maa3) # disagrees with LRT## Data: sl
## Models:
## maa3: log(amp_acc2) ~ trt + IT_imputed + trialNum + (1 + trialNum |
## maa3: beeColHive)
## maa1: log(amp_acc2) ~ trt + IT_imputed + hive + trialNum + (1 + trialNum |
## maa1: beeColHive)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## maa3 9 44032 44105 -22007 44014
## maa1 11 44030 44119 -22004 44008 5.5751 2 0.06157 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(maa3)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: log(amp_acc2) ~ trt + IT_imputed + trialNum + (1 + trialNum |
## beeColHive)
## Data: sl
##
## AIC BIC logLik deviance df.resid
## 44031.7 44104.5 -22006.8 44013.7 24167
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8379 -0.5956 0.1148 0.7380 2.8936
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beeColHive (Intercept) 0.065569 0.25606
## trialNum 0.001557 0.03946 -0.59
## Residual 0.358637 0.59886
## Number of obs: 24176, groups: beeColHive, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.769408 0.440106 4.020
## trthigh 0.181341 0.036211 5.008
## trtlow 0.043373 0.030484 1.423
## IT_imputed 0.308792 0.106513 2.899
## trialNum 0.008597 0.008989 0.956
##
## Correlation of Fixed Effects:
## (Intr) trthgh trtlow IT_mpt
## trthigh 0.122
## trtlow -0.032 0.040
## IT_imputed -0.995 -0.129 0.023
## trialNum 0.050 -0.105 -0.126 -0.100## tt
m13 <- update(maa3, .~. - IT_imputed)
anova(maa3, m13) # remove IT, according to BIC## Data: sl
## Models:
## m13: log(amp_acc2) ~ trt + trialNum + (1 + trialNum | beeColHive)
## maa3: log(amp_acc2) ~ trt + IT_imputed + trialNum + (1 + trialNum |
## maa3: beeColHive)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## m13 8 44037 44102 -22011 44021
## maa3 9 44032 44105 -22007 44014 7.7891 1 0.005256 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# refit with REML = TRUE for paper
m13 <- update(m13, .~., REML = TRUE)
summary(m13)## Linear mixed model fit by REML ['lmerMod']
## Formula: log(amp_acc2) ~ trt + trialNum + (1 + trialNum | beeColHive)
## Data: sl
##
## REML criterion at convergence: 44043.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8402 -0.5958 0.1147 0.7375 2.9643
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## beeColHive (Intercept) 0.077997 0.2793
## trialNum 0.001648 0.0406 -0.54
## Residual 0.358670 0.5989
## Number of obs: 24176, groups: beeColHive, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.039085 0.046419 65.471
## trthigh 0.194605 0.036375 5.350
## trtlow 0.040613 0.030872 1.316
## trialNum 0.009445 0.009264 1.020
##
## Correlation of Fixed Effects:
## (Intr) trthgh trtlow
## trthigh -0.064
## trtlow -0.088 0.035
## trialNum -0.478 -0.112 -0.117# diagnostics
plot(m13)
qqnorm(ranef(m13)$beeColHive[[1]])
qqline(ranef(m13)$beeColHive[[1]])
plot_model(m13, type = "re") # plot random effects to find any outliers
plot_model(m13) # plot fixed effects## Computing p-values via Wald-statistics approximation (treating t as Wald z).
# post-hoc tests -- pvals for paper
summary(glht(m13, linfct = mcp(trt = "Tukey")), test = adjusted("none"))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(amp_acc2) ~ trt + trialNum + (1 + trialNum |
## beeColHive), data = sl, REML = TRUE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## high - full == 0 0.19461 0.03638 5.350 8.8e-08 ***
## low - full == 0 0.04061 0.03087 1.316 0.18833
## low - high == 0 -0.15399 0.04688 -3.285 0.00102 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- none method)# post-hoc tests with bonf adjustment
summary(glht(m13, linfct = mcp(trt = "Tukey")), test = adjusted("bonf"))##
## Simultaneous Tests for General Linear Hypotheses
##
## Multiple Comparisons of Means: Tukey Contrasts
##
##
## Fit: lmer(formula = log(amp_acc2) ~ trt + trialNum + (1 + trialNum |
## beeColHive), data = sl, REML = TRUE)
##
## Linear Hypotheses:
## Estimate Std. Error z value Pr(>|z|)
## high - full == 0 0.19461 0.03638 5.350 2.64e-07 ***
## low - full == 0 0.04061 0.03087 1.316 0.56499
## low - high == 0 -0.15399 0.04688 -3.285 0.00306 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- bonferroni method)Generate CI’s for acceleration
# # set number of bootstrap samples
# nbootSims2 = 1000
#
# sl$trt
#
# # don't need to include hive, but will set trialNum to 2
# pframe <- data.frame(trt = levels((sl$trt)),
# IT_imputed = ITmean, colNum = 99999, trialNum = 2)
# pframe$amp_acc <- 0
# # exponentiate to get back to original scale
# pp <- exp(predict(m13, newdata = pframe, re.form=NA, type = 'response')) # re.form sets all random effects to 0
#
#
# ### Calculate CI's (using bootstrap, not accounting for random effects)
# system.time({
# bb2 <- bootMer(m13, FUN=function(x) predict(x, pframe, re.form=NA, type = 'response'), nsim = nbootSims2)
# })
#
# print(paste("Number of bootstrap samples", nrow(bb2$t)))
# bb2_se <-apply(bb2$t,2,function(x) quantile(x, probs = c(0.025, 0.975)))
#
# #exponentiate to get back to orignal scale
# pframe$blo<-exp(bb2_se[1,])
# pframe$bhi<-exp(bb2_se[2,])
# pframe$predMean <- pp
# pframe <- pframe[, c('trt', "blo", "bhi", "predMean")]
# pframeMake plots for acceleration for paper
# #plot 95% confidence intervals
# # "Mean and bootstrap CI based on fixed-effects uncertainty ONLY"
# pframe$trt3 = plyr::mapvalues(pframe$trt, from = c("full", "high", "low"),
# to = c("Full range\n(220 - 450 Hz)", "High range\n(340 - 390 Hz)", "Low range\n(220 - 330 Hz)"))
#
# pframe
# ga0 <- ggplot(pframe, aes(x=trt3, y=predMean))+
# geom_point()+
# labs( y = expression ("Sonication acceleration "(m~s^{-2})), x = "Frequency range for reward") +
# geom_errorbar(aes(ymin = blo, ymax = bhi), width = 0.1)+
# theme(axis.text.x = element_text(angle = 45, hjust = 1),
# legend.position = 'none') +
# theme_classic() +
# annotate(geom="text", x=c(1,2,3), y=c(0, 0, 0) + 58/2, label=c("a", "b", "a"),
# color="black")
# ga0
# ggsave(plot = ga0, filename = file.path(figDir, "SonicationAmpPredsAndCI_unadjusted.svg"), width = 5, height = 4)
#
#
# # make plot using bonferroni adjustments
#
# ga0 <- ggplot(pframe, aes(x=trt, y=predMean))+
# geom_point()+
# labs( y = expression ("Sonication acceleration "(m~s^{-2})), x = "Frequency range for reward") +
# geom_errorbar(aes(ymin = blo, ymax = bhi), width = 0.1)+
# theme(axis.text.x = element_text(angle = 45, hjust = 1),
# legend.position = 'none') +
# theme_classic() +
# annotate(geom="text", x=c(1,2,3), y=c(0, 0, 0) + 58/2, label=c("a", "b", "a"),
# color="black")
# ga0
# ggsave(plot = ga0, filename = file.path(figDir, "SonicationAmpPredsAndCI_BonfAdjusted.svg"), width = 5, height = 4)Make acceleration plot the same as the frequency plot
# nbootSims = 10
# don't need hive, because that's not in the model we chose (above)
pframe_a <- data.frame(expand.grid(trt = levels(sl$trt),
IT_imputed = seq(min(s2$IT_imputed), max(s2$IT_imputed),
length.out = 50),
beeColHive = 99999,
trialNum = 2))
pframe_a$acc <- 0
pp <- predict(maa3, newdata = pframe_a, re.form=NA, type = 'response') %>% exp() # re.form sets all random effects to 0
### Calculate CI's (using bootstrap, not accounting for random effects)
system.time({
bb2 <- bootMer(maa3, FUN=function(x) predict(x, pframe_a, re.form=NA, type = 'response'), nsim = nbootSims)
})## user system elapsed
## 570.701 8.189 740.749print(paste("Number of bootstrap samples", nrow(bb2$t)))## [1] "Number of bootstrap samples 1000"bb2_se <-apply(bb2$t,2,function(x) quantile(x, probs = c(0.025, 0.975)))
pframe_a$blo<-bb2_se[1,] %>% exp()
pframe_a$bhi<-bb2_se[2,] %>% exp()
pframe_a$predMean <- pp
pframe_a <- pframe_a[, c('trt',"IT_imputed", "blo", "bhi", "predMean")]
pframe_a## trt IT_imputed blo bhi predMean
## 1 full 3.480000 14.92130 20.49092 17.48249
## 2 high 3.480000 17.52856 24.97131 20.95843
## 3 low 3.480000 15.47934 21.40973 18.25745
## 4 full 3.509796 15.17261 20.54514 17.64409
## 5 high 3.509796 17.82574 25.02496 21.15216
## 6 low 3.509796 15.71701 21.47049 18.42620
## 7 full 3.539592 15.39468 20.56141 17.80717
## 8 high 3.539592 18.09191 25.07171 21.34767
## 9 low 3.539592 15.94283 21.54610 18.59652
## 10 full 3.569388 15.62000 20.65418 17.97177
## 11 high 3.569388 18.39424 25.15343 21.54499
## 12 low 3.569388 16.20354 21.66524 18.76841
## 13 full 3.599184 15.83513 20.73491 18.13789
## 14 high 3.599184 18.66304 25.21563 21.74414
## 15 low 3.599184 16.43740 21.78468 18.94189
## 16 full 3.628980 16.04877 20.79714 18.30554
## 17 high 3.628980 18.91551 25.34512 21.94512
## 18 low 3.628980 16.65401 21.84460 19.11698
## 19 full 3.658776 16.28957 20.88669 18.47474
## 20 high 3.658776 19.16470 25.42224 22.14796
## 21 low 3.658776 16.91001 21.93835 19.29368
## 22 full 3.688571 16.51883 20.98596 18.64551
## 23 high 3.688571 19.41859 25.58659 22.35268
## 24 low 3.688571 17.17045 22.03329 19.47202
## 25 full 3.718367 16.74591 20.99225 18.81785
## 26 high 3.718367 19.73413 25.76232 22.55929
## 27 low 3.718367 17.41308 22.14498 19.65200
## 28 full 3.748163 17.00733 21.02231 18.99179
## 29 high 3.748163 20.02179 25.85800 22.76781
## 30 low 3.748163 17.65803 22.26792 19.83365
## 31 full 3.777959 17.29064 21.08060 19.16733
## 32 high 3.777959 20.26990 25.98432 22.97826
## 33 low 3.777959 17.88915 22.35715 20.01697
## 34 full 3.807755 17.54388 21.25211 19.34450
## 35 high 3.807755 20.54958 26.09108 23.19065
## 36 low 3.807755 18.12204 22.49211 20.20199
## 37 full 3.837551 17.80082 21.41256 19.52331
## 38 high 3.837551 20.82454 26.14029 23.40501
## 39 low 3.837551 18.36044 22.62477 20.38873
## 40 full 3.867347 18.07270 21.46685 19.70376
## 41 high 3.867347 21.05723 26.34821 23.62135
## 42 low 3.867347 18.61702 22.75929 20.57718
## 43 full 3.897143 18.27901 21.62321 19.88589
## 44 high 3.897143 21.31606 26.50560 23.83969
## 45 low 3.897143 18.86327 22.84761 20.76738
## 46 full 3.926939 18.47491 21.81165 20.06970
## 47 high 3.926939 21.57774 26.66368 24.06004
## 48 low 3.926939 19.10755 23.00944 20.95934
## 49 full 3.956735 18.72604 21.98491 20.25521
## 50 high 3.956735 21.84331 26.84828 24.28243
## 51 low 3.956735 19.34762 23.20365 21.15307
## 52 full 3.986531 18.89685 22.13445 20.44243
## 53 high 3.986531 22.08898 27.03069 24.50688
## 54 low 3.986531 19.58479 23.38868 21.34859
## 55 full 4.016327 19.09548 22.28627 20.63138
## 56 high 4.016327 22.32890 27.26157 24.73340
## 57 low 4.016327 19.78835 23.62484 21.54592
## 58 full 4.046122 19.28199 22.47553 20.82209
## 59 high 4.046122 22.55763 27.52937 24.96202
## 60 low 4.046122 19.99061 23.83992 21.74508
## 61 full 4.075918 19.47116 22.63902 21.01455
## 62 high 4.075918 22.79601 27.75349 25.19275
## 63 low 4.075918 20.18524 24.07557 21.94607
## 64 full 4.105714 19.66172 22.83738 21.20879
## 65 high 4.105714 23.03786 28.00207 25.42561
## 66 low 4.105714 20.34556 24.27381 22.14892
## 67 full 4.135510 19.86696 23.07407 21.40483
## 68 high 4.135510 23.20926 28.23438 25.66062
## 69 low 4.135510 20.58605 24.47266 22.35365
## 70 full 4.165306 20.06340 23.30164 21.60268
## 71 high 4.165306 23.38716 28.48427 25.89781
## 72 low 4.165306 20.76111 24.71351 22.56027
## 73 full 4.195102 20.22911 23.53131 21.80235
## 74 high 4.195102 23.53858 28.72998 26.13719
## 75 low 4.195102 20.93004 25.02961 22.76880
## 76 full 4.224898 20.37300 23.77193 22.00388
## 77 high 4.224898 23.80039 29.00964 26.37878
## 78 low 4.224898 21.06414 25.25948 22.97926
## 79 full 4.254694 20.51275 24.00149 22.20727
## 80 high 4.254694 23.96033 29.26724 26.62261
## 81 low 4.254694 21.20817 25.55257 23.19166
## 82 full 4.284490 20.68004 24.25923 22.41253
## 83 high 4.284490 24.17189 29.60224 26.86868
## 84 low 4.284490 21.36220 25.87880 23.40602
## 85 full 4.314286 20.79990 24.56828 22.61970
## 86 high 4.314286 24.37055 29.89195 27.11704
## 87 low 4.314286 21.47099 26.18437 23.62237
## 88 full 4.344082 20.91689 24.88270 22.82877
## 89 high 4.344082 24.61206 30.22787 27.36769
## 90 low 4.344082 21.60822 26.49320 23.84072
## 91 full 4.373878 21.05271 25.24620 23.03979
## 92 high 4.373878 24.80339 30.55329 27.62065
## 93 low 4.373878 21.76850 26.80660 24.06108
## 94 full 4.403673 21.16086 25.61546 23.25275
## 95 high 4.403673 24.99472 30.95177 27.87596
## 96 low 4.403673 21.88560 27.17496 24.28348
## 97 full 4.433469 21.26987 25.99771 23.46768
## 98 high 4.433469 25.03595 31.30776 28.13362
## 99 low 4.433469 22.02170 27.47493 24.50794
## 100 full 4.463265 21.37762 26.37888 23.68459
## 101 high 4.463265 25.19318 31.76450 28.39366
## 102 low 4.463265 22.16118 27.76816 24.73447
## 103 full 4.493061 21.48148 26.78362 23.90352
## 104 high 4.493061 25.36295 32.12684 28.65611
## 105 low 4.493061 22.30518 28.15961 24.96310
## 106 full 4.522857 21.56288 27.13191 24.12446
## 107 high 4.522857 25.46175 32.53351 28.92099
## 108 low 4.522857 22.41326 28.61608 25.19384
## 109 full 4.552653 21.67381 27.54884 24.34745
## 110 high 4.552653 25.55923 32.97136 29.18831
## 111 low 4.552653 22.50606 29.03582 25.42671
## 112 full 4.582449 21.75472 27.93020 24.57250
## 113 high 4.582449 25.73006 33.46356 29.45810
## 114 low 4.582449 22.58562 29.47746 25.66174
## 115 full 4.612245 21.88776 28.28517 24.79963
## 116 high 4.612245 25.92033 33.93648 29.73039
## 117 low 4.612245 22.70949 29.89766 25.89893
## 118 full 4.642041 21.96621 28.69729 25.02885
## 119 high 4.642041 26.04195 34.47191 30.00520
## 120 low 4.642041 22.84392 30.27747 26.13832
## 121 full 4.671837 22.08061 29.17373 25.26020
## 122 high 4.671837 26.19108 35.02290 30.28254
## 123 low 4.671837 22.95575 30.73833 26.37992
## 124 full 4.701633 22.15798 29.60170 25.49369
## 125 high 4.701633 26.30307 35.63409 30.56245
## 126 low 4.701633 23.07707 31.15723 26.62376
## 127 full 4.731429 22.26174 30.03044 25.72933
## 128 high 4.731429 26.39870 36.13582 30.84494
## 129 low 4.731429 23.14064 31.62547 26.86985
## 130 full 4.761224 22.35438 30.42155 25.96715
## 131 high 4.761224 26.43772 36.64869 31.13005
## 132 low 4.761224 23.21742 32.13508 27.11821
## 133 full 4.791020 22.45535 30.84092 26.20717
## 134 high 4.791020 26.52807 37.13539 31.41779
## 135 low 4.791020 23.28967 32.63173 27.36887
## 136 full 4.820816 22.50567 31.26549 26.44941
## 137 high 4.820816 26.64325 37.61637 31.70819
## 138 low 4.820816 23.35399 33.14178 27.62185
## 139 full 4.850612 22.59841 31.74805 26.69389
## 140 high 4.850612 26.76066 38.19087 32.00128
## 141 low 4.850612 23.44550 33.65951 27.87716
## 142 full 4.880408 22.64534 32.25759 26.94062
## 143 high 4.880408 26.87859 38.84060 32.29707
## 144 low 4.880408 23.55927 34.18596 28.13484
## 145 full 4.910204 22.73691 32.76710 27.18964
## 146 high 4.910204 26.99703 39.41824 32.59560
## 147 low 4.910204 23.64358 34.71535 28.39489
## 148 full 4.940000 22.80712 33.25456 27.44096
## 149 high 4.940000 27.15383 39.97824 32.89689
## 150 low 4.940000 23.76831 35.25751 28.65735pframe_a$trt3 = plyr::mapvalues(pframe$trt, from = c("full", "high", "low"),
to = c("Full range\n(220 - 450 Hz)",
"High range\n(340 - 390 Hz)",
"Low range\n(220 - 330 Hz)"))
ga1 <- ggplot(pframe_a, aes(x=IT_imputed, y=predMean, color = trt3, fill = trt3))+
geom_line()+
labs(y = expression ("Sonication acceleration "(m~s^{-2})), x = "Intertegular span (mm)") +
geom_ribbon(aes(ymin = blo, ymax = bhi), alpha = 0.3, color = NA)+
theme(plot.background = element_rect(fill = "transparent",colour = NA),
panel.background = element_rect(fill = "transparent",colour = NA),
legend.position =c(0.2, 0.82),
legend.background = element_rect(fill="transparent", colour = NA),
strip.background = element_blank(),
legend.title = element_text(size = 8),
panel.spacing.x = unit(1, "lines"),
plot.margin = margin(c(0.2,0.51, 0.2, 0.2), unit = "cm")) +
scale_color_viridis_d(name = "Frequency range\nfor reward", end = 0.7, option = "inferno") +
scale_fill_viridis_d(name = "Frequency range\nfor reward", end =0.7, option = 'inferno')
ga1
ggsave(plot = ga1, filename = file.path(figDir, "SonicationAmpPredsAndCI_unadjusted_overlap.png"), unit = "in", dpi = 500,
width = 6.5/2, height = 2.5)
svg(file.path(figDir, "SonicationAmpPredsAndCI_unadjusted_overlap.svg"), width = 6.5/2, height = 4)
ga1
dev.off()## quartz_off_screen
## 2combine freq and amp plots into a single figure
# set ggplot theme
theme_set(theme_classic() + theme(axis.text=element_text(colour="black"), text=element_text(size=10)))
g2 <- g1 +
theme(legend.position = c(1.1,1.1),
legend.direction = "horizontal",
plot.margin = margin(c(1.5,0.51, 0.2, 0.2), unit = "cm"),
legend.title = element_text(size = 10),
legend.text = element_text(size = 10))
ga2 <- ga1 +
theme(legend.position = "none",
plot.margin = margin(c(1.5,0.51, 0.2, 0.2), unit = "cm"))
aa <- plot_grid(g2, ga2 )
aa
svg(file.path(figDir, "Exp1_AccAndFreq.svg"), width = 6.5, height = 4)
aa
dev.off()## quartz_off_screen
## 2Visualize aggregated data for acceleration
aggdata <- aggregate(sl$amp_acc, by=list(colNum = sl$colNum,trialNum =sl$trialNum, trt = sl$trt), FUN=mean, na.rm=TRUE)
colnames(aggdata)[colnames(aggdata) == "x"] = "amp"
aggdata_sd <- aggregate(sl$amp_acc, by=list(colNum = sl$colNum,trialNum =sl$trialNum, trt = sl$trt), FUN=sd, na.rm=TRUE)
colnames(aggdata_sd)[colnames(aggdata_sd) == "x"] = "amp_sd"
aggdata = merge(aggdata, aggdata_sd)
aggdata = aggdata[order(aggdata$colNum, aggdata$trialNum, decreasing = FALSE), ]
agg_sm = aggdata[aggdata$trialNum <= 2, ]
rownames(agg_sm) = 1:nrow(agg_sm)
agg_sm## colNum trialNum trt amp amp_sd
## 1 blue_4 1 full 40.03641 27.005004
## 2 gold_3 1 full 32.58012 13.395404
## 3 goldred_4 1 full 27.49328 8.575236
## 4 green_4 1 full 44.14520 45.654277
## 5 green_4 2 full 19.19985 21.543257
## 6 lime_5 1 full 72.53866 33.940021
## 7 limeblue_5 1 full 49.20279 24.837998
## 8 limeblue_5 2 full 59.04592 36.444346
## 9 limegold_5 1 full 34.95839 26.390545
## 10 limegreen_5 1 full 52.56061 32.949195
## 11 limeorange_5 1 full 43.28056 22.966005
## 12 limeorange_5 2 full 50.75113 25.907474
## 13 limepink_5 1 full 49.57493 19.711454
## 14 limepurple_5 1 full 43.98249 22.702402
## 15 limepurple_5 2 low 39.95722 19.359774
## 16 limepurpleyellow_5 1 full 34.54496 17.151702
## 17 limered_5 1 full 42.27701 20.963674
## 18 limered_5 2 low 40.63711 20.895896
## 19 limesilver_5 1 full 26.24910 7.336471
## 20 limewhite_5 1 full 37.65233 22.862123
## 21 limewhite_5 2 full 40.91118 19.936507
## 22 limeyellow_5 1 full 43.51579 23.156058
## 23 orange_3 1 full 45.95001 22.588043
## 24 orangeblue_5 1 full 51.97426 23.301810
## 25 orangegreen_5 1 full 58.59866 33.410963
## 26 orangepink_5 1 full 50.98635 28.370137
## 27 orangepurple_5 1 full 34.53312 16.739467
## 28 purple_3 1 full 53.61466 21.935890
## 29 redblue_4 1 full 28.88506 11.312274
## 30 redblue_4 2 full 48.61796 25.277613
## 31 redgreen_5 1 full 78.72927 46.273733
## 32 redgreen_5 2 full 70.38830 47.080737
## 33 redpink_5 1 full 74.20294 37.536798
## 34 redpink_5 2 low 56.85081 31.951114
## 35 redpurple_5 1 full 58.06343 40.818616
## 36 redpurple_5 2 high 53.13637 26.813039
## 37 silver_5 1 full 46.34517 30.280907
## 38 white_4 1 full 47.76100 21.162920
## 39 white_4 2 full 46.45015 22.564532
## 40 whiteblue_5 1 full 66.11499 33.532056
## 41 whiteblue_5 2 low 67.00735 29.330027
## 42 whitegold_5 1 full 43.10847 26.148044
## 43 whitegold_5 2 low 45.20369 27.241996
## 44 whitegreen_4 1 full 38.87762 18.184245
## 45 whiteorange_5 1 full 61.63802 39.835205
## 46 whiteorange_5 2 low 45.85399 25.838373
## 47 whitepink_5 1 full 63.53760 35.030181
## 48 whitepink_5 2 high 80.19250 47.237615
## 49 whitepurple_5 1 full 47.77247 35.753931
## 50 whitered_5 1 full 48.37233 25.860700
## 51 whitered_5 2 high 74.79446 45.764818
## 52 whiteyellow_5 1 full 65.50425 32.808944
## 53 whiteyellow_5 2 full 56.00923 33.372943
## 54 yellowblue_5 1 full 52.45834 30.688932
## 55 yellowblue_5 2 high 79.36156 41.580725
## 56 yellowgreen_5 1 full 61.58710 33.835105
## 57 yellowgreen_5 2 full 79.18968 27.697874
## 58 yelloworange_5 1 full 73.32616 36.109449
## 59 yelloworange_5 2 high 54.48161 24.329362
## 60 yellowpink_5 1 full 52.74199 24.869065
## 61 yellowpink_5 2 low 57.76271 29.939244
## 62 yellowpurple_5 1 full 62.77375 29.437162
## 63 yellowpurple_5 2 low 68.98565 35.865129
## 64 yellowred_5 1 full 62.77866 39.753892
## 65 yellowred_5 2 full 47.97187 28.376661agg_sm[duplicated(data.frame(agg_sm$colNum, agg_sm$trialNum)), ]## [1] colNum trialNum trt amp amp_sd
## <0 rows> (or 0-length row.names)ggplot(agg_sm, aes(x = trt, y = amp, fill = trialNum > 1)) +
geom_boxplot(alpha = 0.2) +
geom_point(aes(color = trialNum>1)) +
geom_line(aes(group = colNum))
diffdf <- sapply(unique(agg_sm$colNum), FUN = function(x){
tmp = agg_sm[agg_sm$colNum == x, ]
if(nrow(tmp) <= 1)
diff = NA
else
diff = tmp$amp[tmp$trialNum == 2] - tmp$amp[tmp$trialNum == 1]
return(diff)
})
trtDF = sapply(unique(agg_sm$colNum), FUN = function(x){
tmp = agg_sm[agg_sm$colNum == x, ]
ttrs = paste(tmp$trt[tmp$trialNum == 1], tmp$trt[tmp$trialNum == 2], sep = "_")
return(ttrs)
})
buzzdiffs = data.frame(trtDF, diffdf)
ggplot(buzzdiffs, aes(x = trtDF, y= diffdf)) +
geom_boxplot() +
geom_point()+
labs(y = "acceleration difference m/s/s")## Warning: Removed 19 rows containing non-finite values (stat_boxplot).## Warning: Removed 19 rows containing missing values (geom_point).
agg2 = aggregate(sl$amp_acc, by=list(colNum = sl$colNum, fullTrt = sl$trt == "full", trt = sl$trt), FUN=mean, na.rm=TRUE)
colnames(agg2)[colnames(agg2) == "x"] = "amp"
agg2$trt = as.character(agg2$trt)
diffdf <- t(as.data.frame(t(sapply(unique(agg2$colNum), FUN = function(x){
tmp = agg2[agg2$colNum == x, ]
if(nrow(tmp) <= 1)
return(NA)
if (length(unique(tmp$trt)) > 2){
tmp = tmp[tmp$trt != "full_2", ]
}
diff = tmp$amp[!tmp$fullTrt] - tmp$amp[tmp$fullTrt]
return(diff)
}))))
length(diffdf)## [1] 42trtDF = sapply(unique(agg2$colNum), FUN = function(x){
tmp = agg2[agg2$colNum == x, ]
if (length(unique(tmp$trt)) > 2){
tmp = tmp[tmp$trt != "full_2", ]
}
ttrs = paste(tmp$trt[tmp$fullTrt], tmp$trt[!tmp$fullTrt], sep = "_")
return(ttrs)
})
length(trtDF)## [1] 42buzzdiffs = data.frame(trtDF, diffdf)
tapply(buzzdiffs$diffdf, INDEX = buzzdiffs$trtDF, mean)## full_ full_high full_low
## NA 6.911151 3.249068ggplot(droplevels(buzzdiffs[buzzdiffs$trtDF != "full_", ]), aes(x = trtDF, y= as.numeric(diffdf))) +
geom_boxplot() +
geom_point()+
labs(y = "acceleration difference m/s/s")
Plot acceleration vs. frequency for initial trial, accounting for size
ggplot(sl[sl$trialNum == 1, ], aes(x = freq, y = amp_acc2)) +
geom_point(position = position_jitter(width = 1), alpha = 0.2) +
theme_classic() +
stat_smooth(aes(group = trt), method = "loess", se = TRUE, color = "grey40") +
labs( y = expression ("Sonication acceleration "(m~s^{-2})), x = "Sonication Frequency (Hz)")
ggsave(filename = file.path(figDir, "FreqVAmp_t1.png"), width = 6, heigh = 4, dpi = 500, unit = "in")
ggplot(sl[sl$trialNum == 1, ], aes(x = freq, y = amp_acc2)) +
geom_point(position = position_jitter(width = 1), alpha = 0.2) +
theme_classic() +
facet_wrap(~beeColHive) +
stat_smooth(aes(group = trt), method = "loess", se = TRUE, color = "grey40") +
labs( y = expression ("Sonication acceleration "(m~s^{-2})), x = "Sonication Frequency (Hz)")## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 329.65## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 60.35## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.3108e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 414.12## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 329.65## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 60.35## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 7.3108e-17## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 414.12## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 369.7## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 50.3## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 106.09## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : span too small.
## fewer data values than degrees of freedom.## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 369.7## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 50.3## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 106.09## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 299.9## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 299.9## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 404.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)
ggsave(filename = file.path(figDir, "FreqVAmp_t1_facet.png"), width = 12, heigh = 12, dpi = 500, unit = "in")## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 329.65## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 60.35## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.3108e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 414.12## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 329.65## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 60.35## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 7.3108e-17## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 414.12## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 369.7## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 50.3## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 106.09## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : span too small.
## fewer data values than degrees of freedom.## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used
## at 369.7## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 50.3## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal
## condition number 0## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other
## near singularities as well. 106.09## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 299.9## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 299.9## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 404.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)ggplot(sl, aes(x = freq, y = amp_acc2, color = trt)) +
geom_point(position = position_jitter(width = 1), alpha = 0.01, stroke = 0) +
theme_classic() +
facet_wrap(~beeColHive) +
stat_smooth(aes(color = trt, fill = trt, group = trt), method = "loess", se = FALSE) +
labs( y = expression ("Sonication acceleration "(m~s^{-2})), x = "Sonication Frequency (Hz)")## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 329.65## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 60.35## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.3108e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 414.12## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 369.7## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 50.3## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 106.09## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 299.9## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 299.9## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 404.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)
ggsave(filename = file.path(figDir, "FreqVAmp_t1_facet_trt.png"), width = 12, heigh = 12, dpi = 500, unit = "in")## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 329.65## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 60.35## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 7.3108e-17## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 414.12## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 369.7## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 50.3## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 0## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 106.09## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : span too small. fewer data values than degrees of freedom.## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : at 299.9## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : radius 0.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : all data on boundary of neighborhood. make span bigger## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 299.9## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 0.1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 404.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : zero-width neighborhood. make span bigger## Warning: Computation failed in `stat_smooth()`:
## NA/NaN/Inf in foreign function call (arg 5)mm1 <- lmer(log(amp_acc2) ~ I(scale(freq)) + I(scale(freq)^2) + I(scale(freq)^3) +
I(scale(freq)^4) + (1|beeColHive), data = sl[sl$trialNum == 1, ])
mm2 <- lmer(log(amp_acc2) ~ I(scale(freq)) + I(scale(freq)^2) + I(scale(freq)^3) + (1|beeColHive), data = sl[sl$trialNum == 1, ])
summary(mm2)## Linear mixed model fit by REML ['lmerMod']
## Formula:
## log(amp_acc2) ~ I(scale(freq)) + I(scale(freq)^2) + I(scale(freq)^3) +
## (1 | beeColHive)
## Data: sl[sl$trialNum == 1, ]
##
## REML criterion at convergence: 3533.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5406 -0.6267 0.0696 0.6804 2.4316
##
## Random effects:
## Groups Name Variance Std.Dev.
## beeColHive (Intercept) 0.1014 0.3185
## Residual 0.3290 0.5736
## Number of obs: 1971, groups: beeColHive, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.10545 0.05320 58.374
## I(scale(freq)) 0.36999 0.03008 12.299
## I(scale(freq)^2) -0.08814 0.01226 -7.188
## I(scale(freq)^3) -0.03919 0.01056 -3.711
##
## Correlation of Fixed Effects:
## (Intr) I(s()) I(()^2
## I(scl(frq)) -0.046
## I(scl(f)^2) -0.241 0.079
## I(scl(f)^3) 0.011 -0.846 0.065plot(mm2)
plot(x = sl[sl$trialNum == 1, ]$freq, y = exp(predict(mm2, re.form =NA)))
ggplot(sl[sl$trialNum >= 2, ], aes(x = freq, y = amp_acc2)) +
geom_point(position = position_jitter(width = 1), alpha = 0.2, stroke = 0) +
theme_classic() +
facet_wrap(~trt, ncol = 1) +
stat_smooth(aes(color = trt), method = "loess", se = TRUE) +
labs( y = expression ("Sonication acceleration "(m~s^{-2})), x = "Sonication Frequency (Hz)") +
scale_color_viridis_d(name = "Treatment")
ggsave(filename = file.path(figDir, "FreqVAmp_allTrts.png"), width = 6, heigh = 4, dpi = 500, unit = "in")
centerFreq = scale(sl[sl$trialNum == 1, "freq"])
m1 <- lmer(log(amp_acc)~centerFreq + I(centerFreq^2) + I(centerFreq^3) + IT_imputed + (1|beeCol), data = sl[sl$trialNum == 1, ])
summary(m1)## Linear mixed model fit by REML ['lmerMod']
## Formula: log(amp_acc) ~ centerFreq + I(centerFreq^2) + I(centerFreq^3) +
## IT_imputed + (1 | beeCol)
## Data: sl[sl$trialNum == 1, ]
##
## REML criterion at convergence: 3526.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5361 -0.6344 0.0693 0.6857 2.4239
##
## Random effects:
## Groups Name Variance Std.Dev.
## beeCol (Intercept) 0.08395 0.2897
## Residual 0.32877 0.5734
## Number of obs: 1971, groups: beeCol, 42
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.00589 0.57016 3.518
## centerFreq 0.37124 0.03004 12.357
## I(centerFreq^2) -0.08590 0.01227 -7.003
## I(centerFreq^3) -0.03835 0.01055 -3.636
## IT_imputed 0.43530 0.13776 3.160
##
## Correlation of Fixed Effects:
## (Intr) cntrFr I(F^2) I(F^3)
## centerFreq -0.047
## I(cntrFr^2) -0.093 0.081
## I(cntrFr^3) -0.018 -0.844 0.065
## IT_imputed -0.996 0.043 0.071 0.019plot(m1)
# predict -- using mean IT span
ppdf <- data.frame(centerFreq = sort(unique(centerFreq)), IT_imputed = ITmean, beeCol = 99999, acc_amp = 0)
# exponentiate to get back to original scale
ppdf$predAmp = exp(predict(m1, newdata = ppdf, type = "response", re.form = NA))
cf_Unscaled = ppdf$centerFreq * attr(centerFreq, 'scaled:scale') + attr(centerFreq, 'scaled:center')
ggplot(ppdf, aes(x = cf_Unscaled, y = predAmp))+
geom_line() +
labs(x = "Sonication Frequency (Hz)", y = expression ("Sonication acceleration "(m~s^{-2})) ) +
geom_point(data = sl[sl$trialNum == 1, ],
aes(x = freq, y = amp_acc),
alpha = 0.2, position = position_jitter(width =2), pch =16, size = .5) 
ggsave(filename = file.path(figDir, "FreqVsAmp_1stTrial_rawData.svg"), width = 4, height = 3)
g22 <- ggplot(ppdf, aes(x = cf_Unscaled, y = predAmp))+
geom_line() +
labs(x = "Sonication Frequency (Hz)", y = expression ("Predicted sonication acceleration "(m~s^{-2})) )
g22
ggsave(filename = file.path(figDir, "FreqVsAmp_1stTrial.svg"), width = 4, height = 3)
ggplot(sl, aes(x = trt2, y = IT_imputed)) +
geom_point()
analyze time between buzzes and num buzzes between rewards
# convert time to datetime format
# fresh dataset
sl <- read_csv(file.path(dataDir, '01_CombinedTrials_cleaned.csv'))
sl <- sl %>%
mutate(beeColHive = interaction(beeCol, hive))
options(digits.secs=6)
sl$dateTime_fmt= as.POSIXct(strptime(x = sl$datetime_str, format = "%Y-%m-%d %H:%M:%OS" ))
sl %>% head
sl$rewTF
colnames(sl)
# calculate time since beginning of trial
trialStart <- sl %>%
# remove two trials I messed up
filter(!(beeCol == "whitepink" & trialNum == 3)) %>%
filter(!(beeCol == "limepurple" & trialNum == 3)) %>%
dplyr::select(beeColHive, trialNum, dateTime_fmt) %>%
filter(trialNum <= 10) %>%
group_by(beeColHive, trialNum) %>%
slice(1:1) %>%
rename(trialStart = dateTime_fmt) %>%
full_join(sl) %>%
mutate(timeSinceStart = difftime(dateTime_fmt , trialStart, unit = 'sec')) %>%
dplyr::select(beeColHive, trialNum, timeSinceStart, rewTF, dateTime_fmt, trt) %>%
filter(rewTF == TRUE) %>%
mutate(timeSinceReward = timeSinceStart - lag(timeSinceStart)) %>%
arrange((dateTime_fmt)) %>%
group_by(beeColHive) %>% mutate(index = row_number()) %>%
filter(!is.na(timeSinceReward)) %>%
#filter(timeSinceReward < 100000) %>%
filter(beeColHive %in% .$beeColHive[.$trialNum > 1])
# note, use bb from above
# bb <- trialStart %>%
# dplyr::select(beeColHive, trt, trialNum) %>%
# dplyr::filter(trialNum == 2 | trialNum == 3) %>%
# group_by(beeColHive) %>%
# slice(1:1) %>%
# dplyr::arrange(trt)
#
# bb
trialStart <- trialStart %>%
ungroup() %>%
mutate(beeColHive = factor(beeColHive, levels = bb$beeColHive),
t5 = plyr::mapvalues(.$trt, from = c("full", "high", "low"),
to = c("Full range\n(220 - 450 Hz)",
"High range\n(340 - 390 Hz)", "Low range\n(220 - 330 Hz)"))) %>%
filter(trialNum <= 10)
s44 <- trialStart %>%
# filter(trialNum == 1) %>%
group_by(beeColHive, trialNum, t5) %>%
dplyr::summarise(meanInitialTime = mean((as.numeric(timeSinceReward))))
s44
ggplot(trialStart, aes(x = trialNum, y = as.numeric(timeSinceReward))) +
geom_point(alpha = 0.2, aes(color = t5, shape = t5)) +
facet_wrap(~beeColHive, ncol = 8) +
scale_color_viridis_d(name = "",
option = "magma", begin = 0.3,
end = 0.80,
guide = guide_legend(override.aes = list(alpha = 1, size = 4))) +
scale_shape_manual(name = "", values = c(16,17,15)) +
#scale_y_log10() +
labs(x = "Trial Number", y = "Time between rewards (s)") +
theme_classic() +
theme(strip.background = element_blank(),
strip.text = element_blank(),
panel.border = element_rect(colour = "grey40",
fill=NA, size=0.5),
legend.position = 'top') +
# stat_smooth(method = 'loess', se = FALSE, aes(color = t5)) +
geom_hline(data = s44[s44$trialNum == 1,], aes(yintercept = as.numeric(meanInitialTime))) +
geom_line(data = s44, aes(y = as.numeric(meanInitialTime), x = trialNum),lwd = 0.8, show.legend = FALSE) +
geom_line(data = s44, aes(y = as.numeric(meanInitialTime), x = trialNum, color = t5), lwd = 0.8, show.legend = FALSE) +
geom_point(data = s44, aes(y = as.numeric(meanInitialTime), x = trialNum), pch = 18) +
scale_x_continuous(breaks = seq(2, 10, 2)) +
ylim(c(-1, 100))
ggsave(filename = file.path(figDir,"timeBWRewards.png"),
dpi = 500, width = 6.5*1.5, height = 4*1.5, units = "in")
timeSinceStart = list()
timeDiff = list()
timeSinceReward = list()
IDS = character()
prevRewardTime = NA
buzzesSinceReward = list()
# calculate num of buzzes since last reward
for(ii in 1:nrow(sl)){
tmp = sl[ii, c("BeeNumCol", "dateTime_fmt", "rewTF", "index")]
if(!(tmp$BeeNumCol %in% IDS)){
timeSinceStart[ii] = difftime(tmp$dateTime_fmt, tmp$dateTime_fmt, units = "min")
timeDiff[ii] = difftime(tmp$dateTime_fmt, tmp$dateTime_fmt, units = "min")
timeSinceReward[ii] = difftime(tmp$dateTime_fmt, tmp$dateTime_fmt, units = "min")
buzzesSinceReward[ii] = NA
if(as.character(tmp$rewTF) == " T"){
prevRewardTime = tmp$dateTime_fmt
prevRewardBuzzNum = tmp$index
}
else{
prevRewardTime = NA
prevRewardBuzzNum = NA
}
startTime = tmp$dateTime_fmt
IDS = append(IDS, as.character(tmp$BeeNumCol))
}
else{
timeSinceStart[ii] = difftime(tmp$dateTime_fmt, startTime, units = "min")
timeDiff[ii] = difftime(tmp$dateTime_fmt, prevTime, units = "min")
timeSinceReward[ii] = difftime(tmp$dateTime_fmt, prevRewardTime, units = "min")
buzzesSinceReward[ii] = tmp$index - prevRewardBuzzNum
if(tmp$rewTF == " T"){
prevRewardTime = tmp$dateTime_fmt
prevRewardBuzzNum = tmp$index
}
}
prevTime = tmp$dateTime_fmt
}
sl$timeSinceStart = unlist(timeSinceStart)
sl$timeSinceLastBuzz = unlist(timeDiff)
sl$timeSinceReward = unlist(timeSinceReward)
sl$buzzesSinceReward = unlist(buzzesSinceReward)
#plot(sl$index)
sl %>% head
plot(sl$timeSinceStart[1:500])
plot(sl$timeSinceLastBuzz[1:1000])
plot(na.omit(sl$buzzesSinceReward[1:5000]), xlim = c(0, 5000))
ggplot(data = sl[1:10000, ], mapping = aes(x = index, y = timeSinceReward, color= trt)) +
# geom_boxplot() +
geom_point() +
facet_wrap(~trt) +
scale_y_log10() +
theme(legend.position = "none") +
geom_smooth(color = 'grey40') +
scale_color_viridis_d(option = "magma", end = 0.9)
ggplot(data = sl[1:10000, ], mapping = aes(x = index, y = timeSinceReward, color= trt)) +
# geom_boxplot() +
geom_point() +
facet_wrap(~trt) +
scale_y_log10() +
theme(legend.position = "none") +
geom_smooth(color = 'grey40') +
scale_color_viridis_d(option = "magma", end = 0.9)
# plot time vs. reward
ggplot(data = sl[1000:2000, ], mapping = aes(x = timeSinceStart, y = timeSinceReward, color= trt)) +
# geom_boxplot() +
geom_point(alpha = 0.5) +
geom_line() +
facet_wrap(~BeeNumCol, scales = 'free') +
ylim(-0.1, 1.1) +
#geom_smooth() +
#theme(legend.position = "none") +
scale_color_viridis_d(option = "magma", end = 0.9, begin = 0.2,direction = -1)
# plot buzzes b/w rewards
ggplot(data = sl[grepl(pattern = "t", ignore.case = TRUE, x = sl$rewTF) & !(sl$trt %in% c("full", "full_2")), ][1:5000,], mapping = aes(x = index, y = buzzesSinceReward, color= trt)) +
# geom_boxplot() +
geom_point(alpha = 0.5) +
geom_line() +
facet_wrap(~BeeNumCol, scales = 'free_x') +
#ylim(-0.1, 1.1) +
#geom_smooth() +
#theme(legend.position = "none") +
scale_color_viridis_d(option = "magma", end = 0.9, begin = 0.2,direction = -1)
unique(sl[1000:5000, ]$BeeNumCol)
# View(sl[sl$BeeNumCol == "Beelimered11_16Dec2016_Hive5_low", ])
# View(sl[grepl(pattern = "redpink1", x = as.character(sl$BeeNumCol)), ])
ggplot(data = sl[sl$BeeNumCol == "Beelimered11_16Dec2016_Hive5_low", ], mapping = aes(x = index, y = buzzesSinceReward, color= trt)) +
# geom_boxplot() +
geom_point(alpha = 0.5) +
geom_line() +
facet_wrap(~BeeNumCol, scales = 'free') +
#ylim(-0.1, 1.1) +
#geom_smooth() +
#theme(legend.position = "none") +
scale_color_viridis_d(option = "magma", end = 0.9, begin = 0.2,direction = -1)
ggplot(data = sl[grepl(pattern = "redpink", x = as.character(sl$BeeNumCol)), ], mapping = aes(x = index, y = buzzesSinceReward, color= trt)) +
# geom_boxplot() +
geom_point(alpha = 0.5) +
geom_line() +
facet_wrap(~BeeNumCol, scales = 'free') +
#ylim(-0.1, 1.1) +
#geom_smooth() +
#theme(legend.position = "none") +
scale_color_viridis_d(option = "magma", end = 0.9, begin = 0.2,direction = -1)
ggplot(sl[, ], aes(x = index, y = grepl("t", x = rewTF, ignore.case = TRUE)*1)) +
geom_point() +
geom_smooth() +
facet_wrap(~trt, scales = 'free')
binomial_smooth <- function(...){
geom_smooth(method = "glm", method.args = list(family = "binomial"), ...)
}
ggplot(sl[, ], aes(x = timeSinceStart, y = grepl("t", x = rewTF, ignore.case = TRUE)*1)) +
geom_point() +
binomial_smooth() +
facet_wrap(~trt, scales = 'free')
ggplot(sl[sl$trt2 %in% c("low", "high"), ], aes(x = index, y = grepl("t", x = rewTF, ignore.case = TRUE)*1)) +
geom_point() +
binomial_smooth(aes(color = as.factor(trialNum)), se = FALSE) +
facet_wrap(~interaction(beeCol , hive, trt), scales = 'free') +
scale_color_viridis_d(end = 0.9, option = "magma", direction = -1)
# refref: visualize using overall buzz number (not reset for each trial), but still
# color by trial
IDS = character()
sl <- sl[order(sl$dateTime_fmt), ]
ID1 = interaction(sl$beeCol, sl$hive, sl$trt2)
buzzctr = 0
overallBuzz = numeric(length = nrow(sl))
for(ii in 1:nrow(sl)){
tmp = ID1[ii]
if(tmp %in% IDS){
buzzctr = max(overallBuzz[ID1 == tmp]) + 1
}
else{
buzzctr = 0
IDS = append(IDS, as.character(tmp))
}
overallBuzz[ii] = buzzctr
print(ii)
}
sl$overallBuzz = overallBuzz
ggplot(sl[sl$trt2 != "full", ], aes(x = overallBuzz,
y = grepl("t", x = rewTF, ignore.case = TRUE)*1,
color = interaction(beeCol, hive, trt2, trialNum))) +
geom_point() +
geom_smooth(se = FALSE) +
theme(legend.position = "none") +
facet_wrap(~interaction(beeCol, hive, trt2), scales = 'free')
ggplot(sl[sl$trt2 != "full", ], aes(x = overallBuzz,
y = freq,
color = grepl("t", x = rewTF, ignore.case = TRUE))) +
geom_point() +
stat_smooth(method = "loess", se = FALSE, color = "grey40", span = 0.9, lwd= 5) +
#theme(legend.position = "none") +
facet_wrap(~interaction(beeCol, hive, trt2), scales = 'free') +
scale_color_viridis_d(name = "Reward")
ggplot(sl[sl$trt2 != "full", ], aes(x = overallBuzz,
y = freq)) +
geom_ribbon(aes(ymin=lowFrq,ymax=highFrq), fill="grey10", alpha= 0.9, color = NA) +
geom_hex(binwidth = c(150, 10)) +
#theme(legend.position = "none") +
facet_wrap(interaction(beeCol, hive, trt2)~., ncol = 6) +
scale_fill_viridis_c(name = "Num buzzes") +
geom_hline(aes(yintercept = lowFrq), color = 'grey40', size = 1, lty = 2) +
geom_hline(aes(yintercept = highFrq), color = 'grey40', size = 1, lty = 2)
# refref calculate time differences b/w rewards
ggplot(sl[grepl(pattern = "t", x = as.character(sl$rewTF), ignore.case = TRUE) & sl$trt != "full", ], aes(x = as.factor(trialNum), y = timeSinceReward)) +
geom_point(aes(color= as.factor(trialNum))) +
facet_wrap(interaction(beeCol, hive, trt2)~., ncol = 6, scales = 'free_x') +
ylim(c(0,1))
# num buzzes b/w rewards
ggplot(sl[grepl(pattern = "t", x = as.character(sl$rewTF), ignore.case = TRUE) & sl$trt2 != "full", ], aes(x = overallBuzz, y = buzzesSinceReward)) +
geom_point() +
facet_wrap(interaction(beeCol, hive, trt2)~., ncol = 6)
# num buzzes b/w rewards
ggplot(sl[grepl(pattern = "t", x = as.character(sl$rewTF), ignore.case = TRUE) & sl$trt2 != "full", ], aes(x = interaction(index < 50, as.factor(trialNum)), y = buzzesSinceReward, fill = index < 50)) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90)) +
facet_wrap(interaction(beeCol, hive, trt2)~., ncol = 6, scales = 'free_x') +
scale_color_viridis_d()
#refref: calculate continuous time for each bee
sl$continuousTime = 0
sl$beeID = interaction(sl$beeCol, sl$hive)
jj = "orange.3"
kk = 1
for(jj in unique(sl$beeID)){
tmp = sl[sl$beeID == jj, ]
tmp %>% head()
# sort
tmp <- tmp[order(tmp$dateTime_fmt), ]
times = numeric()
trials = numeric()
kk = 1
for(kk in 1:nrow(tmp)){
if(!(tmp$trialNum[kk] %in% trials)){
trials <<- append(trials, tmp$trialNum[kk])
if(kk == 1){
times[kk] = 0
}
else{
times[kk-1] = max(times)
}
}
if(kk == 1){
times[kk] = 0
}
else{
times[kk] = tmp$timeSinceLastBuzz[kk] + times[kk -1]
}
}
tmp$continuousTime = times
#plot(times)
sl <- merge(sl, tmp, all = TRUE)
}
sl %>% head()
# proportion of rewarded buzzes over time
ggplot(sl, aes(x = timeSinceStart, y = grepl("t", x = rewTF, ignore.case = TRUE)* 1)) +
geom_point() +
theme(legend.position = "none") +
stat_smooth(method = "loess", aes(color = interaction(beeCol, hive, trt2, trialNum)), se = FALSE) +
facet_wrap(interaction(beeCol, hive, trt2)~., ncol = 6, scales = 'free_x') +
scale_color_viridis_d() + ylim(c(0, 1))sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] gdtools_0.1.7 bindrcpp_0.2.2 cowplot_0.9.3 effects_4.0-3
## [5] carData_3.0-1 multcomp_1.4-8 TH.data_1.0-9 MASS_7.3-50
## [9] survival_2.42-6 mvtnorm_1.0-8 sjPlot_2.6.0 lme4_1.1-18-1
## [13] Matrix_1.2-14 reshape2_1.4.3 forcats_0.3.0 stringr_1.3.1
## [17] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1 tidyr_0.8.1
## [21] tibble_1.4.2 ggplot2_3.0.0 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-137 lubridate_1.7.4 RColorBrewer_1.1-2
## [4] httr_1.3.1 rprojroot_1.3-2 tools_3.5.1
## [7] TMB_1.7.14 backports_1.1.2 utf8_1.1.4
## [10] R6_2.2.2 sjlabelled_1.0.14 lazyeval_0.2.1
## [13] colorspace_1.3-2 nnet_7.3-12 withr_2.1.2
## [16] tidyselect_0.2.4 mnormt_1.5-5 emmeans_1.2.4
## [19] compiler_3.5.1 cli_1.0.0 rvest_0.3.2
## [22] xml2_1.2.0 sandwich_2.5-0 labeling_0.3
## [25] scales_1.0.0 psych_1.8.4 ggridges_0.5.1
## [28] digest_0.6.16 foreign_0.8-71 minqa_1.2.4
## [31] svglite_1.2.1 rmarkdown_1.10 stringdist_0.9.5.1
## [34] pkgconfig_2.0.2 htmltools_0.3.6 pwr_1.2-2
## [37] rlang_0.2.2 readxl_1.1.0 rstudioapi_0.7
## [40] bindr_0.1.1 zoo_1.8-3 jsonlite_1.5
## [43] magrittr_1.5 modeltools_0.2-22 bayesplot_1.6.0
## [46] fansi_0.3.0 Rcpp_0.12.18 munsell_0.5.0
## [49] prediction_0.3.6 stringi_1.2.4 yaml_2.2.0
## [52] snakecase_0.9.2 plyr_1.8.4 grid_3.5.1
## [55] parallel_3.5.1 sjmisc_2.7.5 crayon_1.3.4
## [58] lattice_0.20-35 ggeffects_0.6.0 haven_1.1.2
## [61] splines_3.5.1 sjstats_0.17.1 hms_0.4.2
## [64] knitr_1.20 pillar_1.3.0 estimability_1.3
## [67] codetools_0.2-15 stats4_3.5.1 glue_1.3.0
## [70] evaluate_0.11 data.table_1.11.4 modelr_0.1.2
## [73] nloptr_1.0.4 cellranger_1.1.0 gtable_0.2.0
## [76] assertthat_0.2.0 coin_1.2-2 xtable_1.8-2
## [79] broom_0.5.0 survey_3.33-2 coda_0.19-1
## [82] viridisLite_0.3.0 glmmTMB_0.2.2.0